AGI & CoCoSci

Department of Psychology, UC Berkeley***, Cognitive Development & Learning Lab (Gopnik Lab) - ***UC Berkeley***.

Department of Electrical Engineering and Computer Science, UC Berkeley***, Interactive Autonomy and Collaborative Technologies Laboratory (InterACT) - ***UC Berkeley***.

Department of Electrical Engineering and Computer Science, CSAIL, MIT***, Computer-Aided Programming Group - ***MIT***.

Department of Psychology, NYU***, Human & Machine Learning Lab (Lake Lab) - ***NYU***.

Department of Psychology, UC Berkeley***, Kidd Lab - ***UC Berkeley***.

MIT***.

Department of Statistics, UCLA***.

Department of Psychological and Brain Sciences, Johns Hopkins University (JHU)***, Hopkins Perception & Mind Lab - ***JHU***.

Computer Science Department, Stanford***, Intelligence through Robotic Interaction at Scale (IRIS Group) - ***Stanford***.

Department of Psychology, UCSD***, Computational Cognition Lab - ***UCSD***.

Department of Psychology, Harvard***, Harvard Laboratory for Developmental Studies - ***Harvard***.

Department of Computer Science, Courant Institute of Mathematical Sciences, NYU***.

Department of Psychology, UC Berkeley***, Berkeley Early Learning Lab (Xu Lab) - ***UC Berkeley***.

Department of Psychology, Harvard***, Moral Psychology Research Lab - ***Harvard***.

Department of Psychology, NYU***.

Department of Computer Science, UCLA***, StarAI Lab - ***UCLA***.

Department of Psychology, Department of Statistics, UCLA***, Computational Vision and Learning Lab (CVL) - ***UCLA***.

Department of Communication, Stanford***, Virtual Human Interaction Lab (VHIL) - ***Stanford***.

Computer Science Department, Stanford***.

Department of Brain and Cognitive Sciences, CSAIL, MIT***, Computational Cognitive Science Group (CoCoSci Group) - ***MIT***.

Department of Psychology, Stanford***, Cognitive Tools Lab - ***Stanford***.

Department of Brain and Cognitive Sciences, MIT***, Early Childhood Cognition Lab - ***MIT***.

Department of Electrical Engineering and Computer Science, CSAIL, MIT***, The Learning & Intelligent Systems Group - ***MIT***.

Computer Science Department, Human-Centered AI Institute, Stanford***, Stanford Vision and Learning Lab - ***Stanford***.

Department of Computer Science, Stevens Institute of Technology (SIT)***, Computation and Decision-Making Lab - ***SIT***.

Department of Psychology, Stanford***, The Stanford Language and Cognition Lab - ***Stanford***.

Department of Psychology, Computer Science Department, Stanford***, Computation & Cognition Lab (CoCoLab) - ***Stanford***.

Department of Brain and Cognitive Sciences, MIT***, Social Cognitive Neuroscience Laboratory (SaxeLab) - ***MIT***.

Department of Psychology, Harvard***, Computational Cognitive Neuroscience Lab (CCN Lab) - ***Harvard***.

School of AI and Institute for AI, Peking University (PKU)***.

Department of Psychology, UC Berkeley***, The computation and language lab (colala) - ***UC Berkeley***.

Department of Psychology, Princeton***, Concepts & Cognition Lab - ***Princeton***.

Department of Statistics, Department of Psychology, UCLA***, Visual Intelligence Lab - ***UCLA***.

Department of Psychology, Department of Computer Science, Princeton***, Computational Cognitive Science Lab - ***Princeton***.

Department of Psychology, Stanford***, Causality in Cognition Lab (CICL) - ***Stanford***.

Department of Psychology, NYU***, Computation & Cognition Lab - ***NYU***.

Department of Psychology, Harvard***, Computation, Cognition, and Development Lab (CoCoDev) - ***Harvard***.

Department of Psychology, Center for Neural Science, NYU***, Wei Ji Ma Lab - ***NYU***.

IDG/McGovern Institute for Brain Research and the State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University (BNU)***, Yanchao Bi's Concept Lab (Bi Lab) - ***BNU***.

Department of Statistics, UCLA***.

School of AI and Institute for AI, Peking University (PKU)***, Cognitive Reasoning Lab (CoRe Lab) - ***PKU***.

Department of Computer Science, UCSD***, Machine Learning, Perception, and Cognition Lab (mlPC) - ***UCSD***.

CHI EA'24***, 2024. [All Versions]. [Preprint]. Large language models can produce creative and diverse responses. However, to integrate them into current developer workflows, it is essential to constrain their outputs to follow specific formats or standards. This work surveyed 51 experienced industry professionals to understand the range of scenarios and motivations driving the need for output constraints from a user-centered perspective. The authors identified 134 concrete use cases for constraints at t...

Nature***, 2016. [All Versions].

Journal of Cognition and Development***, 2009. [All Versions].

RSS'20***, 2020. [All Versions]. A system for modeling 3D dynamic scene graphs on multiple levels (metric-semantic mesh, objects and agents, places and structures, rooms, and buildings).

CogSci'95***, 1995. [All Versions]. Extending the dual space search.

The Annals of Statistics***, 1973. [All Versions]. [Preprint]. A classic review on non-parametric problems.

NeurIPS'20***, 2020. [All Versions].

MIT Press***, 1985. [All Versions]. A cognitive account on Metaphor.

Communications of the ACM***, 2023. [All Versions].

The nature of creativity: Contemporary psychological perspectives***, 1988. [All Versions]. A computational account on insights for scientific discovery.

Behavior Research Methods***, 2022. [All Versions]. Model-based strategy identification.

Neuron***, 2012. [All Versions].

ECCV'18***, 2018. [Project].

AAAI'17***, 2017.

Child Development***, 2010. [All Versions].

ETFA'21***, 2021. [All Versions]. This paper presents the design of the PPR-DSL to effectively and efficiently represent Product-Process-Resource (PPR) aspects and evaluate constraints defined for modeling PPR views in the Formalized Process Description standard (VDI 3682).

Precision Agriculture***, 2020. [All Versions]. This paper proposes a domain-specific language framework for the design and development of precision-agriculture FMISs, which copes with challenges on supporting the understandability, enhancing communication and analysis of the design decisions, and the communication among stakeholders.

International Journal of Human-Computer Studies***, 1996. [All Versions]. This paper describes a cognitive model of exploratory learning, which covers both trial-and-error and instruction-taking activities. The model, implemented in Soar, is grounded in empirical data of subjects in a task-oriented, trial-and-error exploratory learning situation. A key empirical finding reflected in the model is the repeated scanning of a subset of the available menu items, with increased attention to items on each succe...

Nature Communications***, 2024. [All Versions]. This work employs ontologies to capture data and material flows in design-make-test-analyse cycles, utilising autonomous agents as executable knowledge components to carry out the experimentation workflow. Data provenance is recorded to ensure its findability, accessibility, interoperability, and reusability. The architecture is built upon the World Avatar project, which seeks to create an all-encompassing digital twin based on a dynamic knowledge graph.

Nature***, 2023. [All Versions]. This paper presents RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels.

NeurIPS'19***, 2019. [All Versions].

Science***, 2017. [All Versions]. [Preprint]. Learning from a few examples and generalizing to markedly different situations are capabilities of human visual intelligence that are yet to be matched by leading machine learning models. By drawing inspiration from systems neuroscience, this work introduces a probabilistic generative model for vision in which message-passing–based inference handles recognition, segmentation, and reasoning in a unified way. The model demonstrates excellent generalization and ...

Science***, 2000. [All Versions]. The original paper on spectrum clustering.

2022. [All Versions]. This work presents a grammar for expressing hypotheses in visual data analysis to formalize the previously abstract notion of "analysis tasks." Through the lens of this grammar, the authors lay the groundwork for how a user's data analysis questions can be operationalized and automated as a set of hypotheses (a hypothesis space). The authors demonstrate that the grammar-based approach for analysis tasks can provide a systematic method towards unifying three disparate spaces in visua...

2022. [All Versions]. Combining a basic set of building blocks into more complex forms is a universal design principle. Most protein designs have proceeded from a manual bottom-up approach using parts created by nature, but top-down design of proteins is fundamentally hard due to biological complexity. This work demonstrates how the modularity and programmability long sought for protein design can be realized through generative artificial intelligence. Advanced protein language models demonstrate emergen...

Nature Communications***, 2024. [All Versions]. Autonomous reaction network exploration algorithms offer a systematic approach to explore mechanisms of complex chemical processes. However, the resulting reaction networks are so vast that an exploration of all potentially accessible intermediates is computationally too demanding. This paper introduces a STEERING WHEEL to guide an otherwise unbiased automated exploration. The STEERING WHEEL algorithm is intuitive, generally applicable, and enables one to f...

ICML'21***, 2021. [All Versions]. [Project]. This paper presents Omega, a probabilistic programming language with support for counterfactual inference. This feature is accomplished by introducing a new operator to probabilistic programming akin to Pearl’s do.

ICSE'24***, 2024. [All Versions]. A survey finding that developers are most motivated to use AI programming assistants because they help developers reduce key-strokes, finish programming tasks quickly, and recall syntax, but resonate less with using them to help brainstorm potential solutions.

ACS Synthetic Biology***, 2018. [All Versions]. This paper presents a logic programming language that allows a broad range of computational nucleic acid systems to be designed and analyzed. The language extends standard logic programming with a novel equational theory to express nucleic acid molecular motifs. It automatically identifies matching motifs present in the full system, in order to apply a specified transformation expressed as a logical rule.

Nature Human Behavior***, 2017. [All Versions].

The Bell System Technical Journal***, 1948. [All Versions]. Shannon's original paper on Information Theory.

.pdf) - ***Personality and Social Psychology Bulletin***, 2005. [All Versions].

Nature***, 2020. [All Versions]. [Preprint]. This work uses a mobile robot to search for improved photocatalysts for hydrogen production from water. The robot operated autonomously over eight days, performing 688 experiments within a ten-variable experimental space, driven by a batched Bayesian search algorithm. This autonomous search identified photocatalyst mixtures that were six times more active than the initial formulations, selecting beneficial components and deselecting negative ones.

Journal of Pragmatics***, 2014. [All Versions].

Science***, 2023. [All Versions]. [Code]. [Data (Reproduced)]. [Preprint]. [GoodScents Database]. [Leffingwell Database]. Mapping molecular structure to odor perception is a key challenge in olfaction. This work used graph neural networks to generate a principal odor map (POM) that preserves perceptual relationships and enables odor quality prediction for previously uncharacterized odorants. The model was as reliable as a human in describing odor quality: On a prospective validation set of 400 out-of-sam...

Cognition***, 2010. [All Versions].

ICAART***, 2021. [All Versions]. A probabilistic perspective for interpreting Abductive Reasoning.

Cognitive Science***, 2008. [All Versions].

ILP'12***, 2012. [All Versions].

Cognition***, 1995. [All Versions].

IEEE Transactions on Robotics***, 2020. [All Versions]. [Preprint]. Tactile sensing is a key sensor modality for robots interacting with their surroundings. These sensors provide a rich and diverse set of data signals that contain detailed information collected from contacts between the robot and its environment. The data are however not limited to individual contacts and can be used to extract a wide range of information about the objects in the environment as well as the actions of the robot during the...

Routledge***, 1956. [All Versions]. This book is a pioneering account of how human beings achieve a measure of rationality in spite of the constraints imposed by time and ignorance.

Journal of Machine Learning Research***, 2017. [All Versions].

College Composition and Communication***, 1965. [All Versions]. The original paper on analyzing the structure of expository paragraphs, with the two patterns---the Topic-Restriction-Illustration pattern and the Problem-Solution pattern.

Quarterly of Applied Mathematics***, 2018. [All Versions]. [Preprint]. The pattern theory of Grenander is a mathematical framework where patterns are represented by probability models on random variables of algebraic structures. In this paper, the authors review three families of probability models, namely, the discriminative models, the descriptive models, and the generative models. A discriminative model is in the form of a classifier. It specifies the conditional probability of the class label given t...

Science Robotics***, 2019. [All Versions]. [Preprint]. The ability to provide comprehensive explanations of chosen actions is a hallmark of intelligence. Lack of this ability impedes the general acceptance of AI and robot systems in critical tasks. This paper examines what forms of explanations best foster human trust in machines and proposes a framework in which explanations are generated from both functional and mechanistic perspectives. The robot system learns from human demonstrations to open medicin...

ACM Computing Surveys***, 2015. [All Versions]. Domain-Specific Aspect Languages (DSALs) are Domain-Specific Languages (DSLs) designed to express crosscutting concerns. Compared to DSLs, their aspectual nature greatly amplifies the language design space. This survey structures this space in order to shed light on and compare the different domain-specific approaches to deal with crosscutting concerns. This survey reports on a corpus of 36 DSALs covering the space, discuss a set of design considerations, a...

Psychonomic Bulletin & Review***, 2014. [All Versions].

Journal of Artificial Intelligence and Consciousness***, 2020. [All Versions].

ICML'16***, 2016. [All Versions]. The authors show that a generative random field model, which they call generative ConvNet, can be derived from the commonly used discriminative ConvNet, by assuming a ConvNet for multi-category classification and assuming one of the category is a base category generated by a reference distribution. For a further assumption that the non-linearity in the ConvNet is Rectified Linear Unit (ReLU) and the reference distribution is Gaussian white noise, then a generative ConvNe...

Psychological Review***, 2022. [All Versions]. A comprehensive review on the perspective of treating analogy as cross-domain generalization.

Knowledge Acquisition***, 1993. [All Versions].

2018. [All Versions]. Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of less than 20 dimensions, and tolerates stochastic noise in function evaluations. It builds a surrogate for the objective and quantifies the uncertainty in that surrogate using a Bayesian machine learning technique, Gaussian process regression, and then uses an acquisition function defined from this ...

Predicting Structured Data, MIT Press***, 2006. [All Versiosn]. Yann LeCun's tutorial on energy-based learning.

Science***, 2020. [All Versions]. [Preprint]. [XDL Documentation]. [XDL Schema Database]. This paper reports a software platform that uses natural language processing to translate the organic chemistry literature directly into editable code, which in turn can be compiled to drive automated synthesis of the compound in the laboratory.

2020. [All Versions].

Plato Stanford***. A computational philosophy account on Abduction, one of the three thinking patterns besides Induction and Deduction, being unique for its potential to introduce new ideas into current knowledge.

Synthese***, 2021. [All Versions]. A computational philosophy account connecting Abduction and scientific thinking.

Computational Logic***, 2002. [All Versions]. [Preprint]. Abduction in Logic Programming started in the late 80s, early 90s, in an attempt to extend logic programming into a framework suitable for a variety of problems in Artificial Intelligence and other areas of Computer Science. This paper aims to chart out the main developments of the field over the last ten years and to take a critical view of these developments from several perspectives: logical, epistemological, computational and suitability to ap...

Synthese***, 2019. [All Versions].

Model-Based Reasoning in Science and Technology***, 2010. [All Versions]. The distinctions and relations between Abduction, Induction, and Analogy.

AAAI'19***, 2019. [All Versions]. The growing range of applications of Machine Learning (ML) in a multitude of settings motivates the ability of computing small explanations for predictions made. Small explanations are generally accepted as easier for human decision makers to understand. Most earlier work on computing explanations is based on heuristic approaches, providing no guarantees of quality, in terms of how close such solutions are from cardinality- or subset-minimal explanations. This paper deve...

Journal of Applied Logic***, 2015. [All Versions].

Springer***, 2009. [All Versions]. Most philosophers of science in the twentieth century have concluded that no logic of creative processes exists and, moreover, that a rational model of discovery is impossible. In short, scientific creative inferences are irrational and there is no “reasoning” to hypotheses. On the other hand, some research in the area of artificial intelligence has shown that methods for discovery could be found that are computationally adequate for rediscovering --- or discovering for...

ICLR'20***, 2020. [All Versions]. Abductive commonsense reasoning on large language models.

Advances in Bayesian Networks***, 2004. [All Versions]. The goal of this paper is to serve as a survey for the problem of abductive inference (or belief revision) in Bayesian networks. Thus, the problem is introduced in its two variants: total abduction (or MPE) and partial abduction (or MAP) . Also, the problem is formulated in its general case, that is, looking for the K best explanations. Then, a (non exhaustive) review of exact and approximate algorithms for dealing with both abductive inference prob...

Synthese***, 2018. [All Versions].

IJCAI'21***, 2021. [All Versions].

Science China Information Sciences***, 2019. [All Versions].

Journal of Logic Computation***, 1992. [All Versions]. This paper is a survey and critical overview of recent work on the extension of logic programming to perform abductive reasoning (abductive logic programming). The authors outline the general framework of abduction and its applications to knowledge assimilation and default reasoning; and they introduce an argumentation-theoretic approach to the use of abduction as an interpretation for negation as failure.

ECML'11***, 2011. [All Versions]. Plan recognition is the task of predicting an agent’s top-level plans based on its observed actions. It is an abductive reasoning task that involves inferring cause from effect. Most existing approaches to plan recognition use either first-order logic or probabilistic graphical models. While the former cannot handle uncertainty, the latter cannot handle structured representations. In order to overcome these limitations, this work develops an approach to plan recognition ...

Springer***, 2000. [All Versions]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.

ICIRA'24***, 2024. [All Versions]. [Preprint]. Crafting automation systems tailored for specific domains requires aligning the space of human experts’ semantics with the space of robot executable actions, and scheduling the required resources and system layout accordingly. Regrettably, there are three major gaps, fine-grained domain-specific knowledge injection, heterogeneity between human knowledge and robot instructions, and diversity of users’ preferences, resulting automation system design a case-by-...

CVPR'21***, 2021. [All Versions].

CogSci'20***, 2020. [All Versions].

Wikipedia***. Wikipedia on the distinctions and the trade-off between accuracy and precision.

Nature***, 2023. [All Versions]. This paer introduces an artificial-intelligence-based method for accurate, medium-range global weather forecasting. It shows that three-dimensional deep networks equipped with Earth-specific priors are effective at dealing with complex patterns in weather data, and that a hierarchical temporal aggregation strategy reduces accumulation errors in medium-range forecasting. Trained on 39 years of global data, the program, Pangu-Weather, obtains stronger deterministic forecast...

The Journal of Logic Programming***, 1999. [All Versions]. This paper presents the framework of Abductive Constraint Logic Programming (ACLP), which integrates Abductive Logic Programming (ALP) and Constraint Logic Programming (CLP). In ACLP, the task of abduction is supported and enhanced by its non-trivial integration with constraint solving. This integration of constraint solving into abductive reasoning facilitates a general form of constructive abduction and enables the application of abduction to c...

AAAI'14***, 2014. [All Versions].

CVPR'20***, 2020. [All Versions].

The Logic Programming Paradigms***, 1999. [All Versions]. [Preprint]. This is a discussion of some of the achievements and challenges related to representing actions and the design of planners from the perspective of logic programming. The authors talk about recent work on action languages and translating them into logic programming, on representing possible histories of an action domain by answer sets, on efficient implementations of the answer set semantics and their use for generating plans, and on ca...

Cognition***, 2009. [All Versions]. [Appendix]. The original paper on Inverse Planning, a computational implementation of Theory of Mind. Humans are adept at inferring the mental states underlying other agents’ actions, such as goals, beliefs, desires, emotions and other thoughts. This paper proposes a computational framework based on Bayesian inverse planning for modeling human action understanding. The framework represents an intuitive theory of intentional agents’ behavior based on the principle of ra...

CVPR'18***, 2018.

Journal of Artificial Intelligence Research***, 2020. [All Versions].

AAMAS'17***, 2017.

CogSci'20***, 2020. [All Versions].

ICML'20***, 2020. [All Versions].

Behavior Research Methods***, 2012. [All Versions]. [Project]. A database for age-of-acquisition ratings for over 30k English words.

ICML'21***, 2021. [All Versions]. Inspired by cognitive development studies on intuitive psychology, this paper presents a benchmark consisting of a large dataset of procedurally generated 3D animations, AGENT (Action, Goal, Efficiency, coNstraint, uTility), structured around four scenarios (goal preferences, action efficiency, unobserved constraints, and cost-reward trade-offs) that probe key concepts of core intuitive psychology. The results suggest that to pass the designed tests of core intuitive psy...

VRST'17***, 2017. [All Versions]. This paper proposes the use of Domain-Specific Languages (DSLs) to ease the description and generation of VR experiments, thus letting experiment designers focus on their core tasks: designing, conducting, and reporting experiments.

Science Advances***, 2019. [All Versions]. A core challenge for both physics and artificial intelligence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle, functions of practical interest often exhibit symmetries, separability, compositionality, and other simplifying properties. In this spirit, the authors develop a recursive multidimensional symbolic regression algorithm that combines neural n...

ACM SIGGRAPH Asia'23***, 2023. [All Versions]. By continually observing their environment and communicating, AI agents trained in the Chinese dictionary and the Nüshu corpus collaborate towards creating a standard writing system to encode Chinese.

IJCAI***.

Allen Institute***. [Paper].

IBM Journal of Research and Development***, 1977. [All Versions]. Chaitin's original paper on Algorithmic Information Theory.

Cognitive Psychology***, 2021. [All Versions].

ICLR'20***, 2020.

IJCAI'13***, 2013. [All Versions]. Abduction has been extensively studied in propositional logic because of its many applications in artificial intelligence. However, its intrinsic complexity has been a limitation to the implementation of abductive reasoning tools in more expressive logics. The authors have devised such a tool in ground flat equational logic, in which literals are equations or disequations between constants. The tool is based on the computation of prime implicates. It uses a relaxed para...

Nature Medicine***, 2019. [All Versions]. The microscopic assessment of tissue samples is instrumental for the diagnosis and staging of cancer, and thus guides therapy. However, these assessments demonstrate considerable variability and many regions of the world lack access to trained pathologists. Though artificial intelligence (AI) promises to improve the access and quality of healthcare, the costs of image digitization in pathology and difficulties in deploying AI solutions remain as barriers to real-...

Nature***, 2023. [All Versions]. This paper introduces the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large...

Nature Chemistry***, 2022. [All Versions]. [Preprint]. This paper presents a portable suitcase-sized chemical synthesis platform containing all the modules required for synthesis and purification. The system uses a chemical programming language coupled to a digital reactor generator to produce reactors and executable protocols based on text-based literature syntheses. Simultaneously, the platform generates a reaction pressure fingerprint, used to monitor processes within the reactors and remotely perform...

ICML'20***, 2020. [All Versions].

Nature Communications***, 2024. [All Versions]. This paper presents a dynamically programmable system capable of making, optimizing, and discovering new molecules which utilizes seven sensors that continuously monitor the reaction. By developing a dynamic programming language, the work demonstrates the 10-fold scale-up of a highly exothermic oxidation reaction, end point detection, as well as detecting critical hardware failures.

Scientific Reports***, 2025. [All Versions]. To enhance aircraft fault diagnosis efficiency, this paper proposes HybridRAG, an intelligent-guided troubleshooting framework that integrates knowledge graphs and large language models (LLMs). Unlike conventional retrieval-augmented generation (RAG) methods that rely on single-modal retrieval, HybridRAG adopts a multi-dimensional retrieval strategy, combining graph-based reasoning with both vector-based and BM25-based text retrieval techniques. This hybrid ap...

Springer***, 2008. [All Versions]. The introductory book for Algorithmic Information Theory, especially the Kolmogorov complexity theory.

2020. [All Versions]. Yaodong Yang's review on multi-agent reinforcement learning from the perspective of game theory.

ICML'19***, 2019. [All Versions]. Explaining the analogy capability in word embeddings.

Plato Stanford***. A computational philosophy account on Analogy, a comparison between two objects, or systems of objects, that highlights respects in which they are thought to be similar.

CogSci'20***, 2020. [All Versions].

Artificial Intelligence***, 2019. [All Versions]. A mathematical account on analogy.

ICML'13***, 2013. [All Versions]. The first application of analogy to machine learning.

Journal of Machine Learning Research***, 2019. [All Versions]. This paper provides new insights on the Unadjusted Langevin Algorithm. The authors show that this method can be formulated as the first order optimization algorithm for an objective functional defined on the Wasserstein space of order $2$. Using this interpretation and techniques borrowed from convex optimization, the authors give a non-asymptotic analysis of this method to sample from log-concave smooth target distribution on $\mathbb{R}^d$....

ICLPNR'99***, 1999. [All Versions]. [Preprint]. The original paper on Answer Set Programming (ASP), a form of declarative programming oriented towards difficult search problems, on the use of nonmonotonic reasoning in knowledge representation. In ASP solutions to a problem are represented by answer sets (known also as stable models), and not by answer substitutions produced in response to a query, as in conventional logic programming.

ICML'04***, 2004. [All Versions]. Pieter Abbeel and Andrew Ng's original paper on inverse reinforcement learning (IRL).

Nature Communications***, 2023. [All Versions]. [Project]. Advances in machine learning (ML) and automated experimentation are poised to vastly accelerate research in polymer science. Data representation is a critical aspect for enabling ML integration in research workflows, yet many data models impose significant rigidity making it difficult to accommodate a broad array of experiment and data types found in polymer science. This inflexibility presents a significant barrier for researchers to leverage th...

Journal of the American Chemical Society***, 2024. [All Versions]. The development of AI synthesis planners trained solely on reaction-example-data has stagnated and is not on par with the performance of “hybrid” algorithms combining AI with expert knowledge. This Perspective examines possible causes of these shortcomings, extending beyond the established reasoning of insufficient quantities of reaction data. Drawing attention to the intricacies and data biases that are specific to the domain of syntheti...

CogSci'16***, 2016. [All Versions]. A behavioral study for the battleship game.

CogSci'19***, 2019. [All Versions].

AAMAS'17***, 2017.

NeurIPS'21***, 2021. [All Versions].

ECCV'18***, 2018. [All Versions].

Nature Machine Intelligence***, 2023. [All Versions]. [Preprint]. This paper introduces ChemCrow, an LLM chemistry agent designed to accomplish tasks across organic synthesis, drug discovery and materials design. By integrating 18 expert-designed tools and using GPT-4 as the LLM, ChemCrow augments the LLM performance in chemistry, and new capabilities emerge. The agent autonomously planned and executed the syntheses of an insect repellent and three organocatalysts and guided the discovery of a novel chro...

ACL'24***, 2024. [All Versions]. [Preprint]. [Project]. The original paper on the automated design of DSLs, referred to as AutoDSL. Accurate representation of procedures in restricted scenarios, such as non-standardized scientific experiments, requires precise depiction of constraints. Unfortunately, Domain-Specific Language (DSL), as an effective tool to express constraints structurally, often requires case-by-case hand-crafting, necessitating customized, labor-intensive efforts. To overcome this challe...

AAAI Spring Symposium Series 2021 on Artificial Intelligence for Synthetic Biology***, 2021. [All Versions]. This work proposes an automated biodesign engineering framework empowered by Abductive Meta-Interpretive Learning (MetaAbd), a novel machine learning approach that combines symbolic and sub-symbolic machine learning, to further enhance the design-build-test-learn cycle by enabling the learning machine to 1) exploit domain knowledge and learn human-interpretable models that are expressed by formal ...

Nature Communications***, 2020. [All Versions]. This paper presents a method to convert unstructured experimental procedures written in English to structured synthetic steps (action sequences) reflecting all the operations needed to successfully conduct the corresponding chemical reactions.

2022. [All Versions]. A comprehensive review on AutoRL.

IJCAI'20***, 2020. [All Versions].

Chemical Science***, 2021. [All Versions]. This perspective provides an overview of the current state of automation of synthetic chemistry at the benchtop scale with a particular emphasis on core considerations and the ensuing challenges of deploying a system. The authors aim to reframe automation as decidedly not automatic but rather an iterative process that involves a series of careful decisions (both human and computational) and constant adjustment.

Nature***, 2023. [All Versions]. An artificial intelligence system driven by GPT-4 that autonomously designs, plans and performs complex experiments by incorporating large language models empowered by tools such as internet and documentation search, code execution and experimental automation.

POPL'23***, 2023. [All Versions]. This paper proposes library learning modulo theory (LLMT), a new library learning algorithm that additionally takes as input an equational theory for a given problem domain. LLMT uses e-graphs and equality saturation to compactly represent the space of programs equivalent modulo the theory, and uses a novel e-graph anti-unification technique to find common patterns in the corpus more directly and efficiently.

NeurIPS'21***, 2021. [All Versions].

00249-6) - ***Trends in Chemistry***, 2023. [All Versions]. This perspective article provides essential insights into the decision-making process for choosing automation platforms, highlighting the suitability of fixed automation for standardized tasks and the strategic use of flexible automation in dynamic research settings.

ICLR'21***, 2021. [All Versions].

Current Opinion in Behavioral Sciences***, 2021. [All Versions].

Cognition***, 2018. [All Versions].

IJCAI'11***, 2011. [All Versions]. [Preprint]. This work introduces Bayesian Abductive Logic Programs (BALP), a probabilistic logic that adapts Bayesian Logic Programs (BLPs) for abductive reasoning. Like BLPs, BALPs also combine first-order logic and Bayes nets. However, unlike BLPs, which use deduction to construct Bayes nets, BALPs employ logical abduction. As a result, BALPs are more suited for problems like plan/activity recognition that require abductive reasoning.

Trends in Cognitive Sciences***, 2016. [All Versions]. A perspective on human probabilistic modeling without explicit probabilistic computation.

Chapman and Hall/CRC***, 1995. [All Versions]. Don Rubin's introductory book on Bayesian models.

Plato Stanford***. A computational philosophy account on the nature of uncertainty modeling in Bayesian Epistemology.

IJCAI'07***, 2007. [All Versions]. A Bayesian account on classic inverse reinforcement learning.

NeurIPS'18***, 2018. [All Versions]. A Bayesian account on MAML.

NeurIPS'98***, 1998. [All Versions]. [Preprint]. This work considers the problem of learning concepts from small numbers of positive examples, a feat which humans perform routinely but which computers are rarely capable of. Bridging machine learning and cognitive science perspectives, this work presents both theoretical analysis and an empirical study with human subjects for the simple task oflearning concepts corresponding to axis-aligned rectangles in a multidimensional feature space. Existing learning...

Proceedings of the National Academy of Sciences***, 2022. [All Versions]. A Bayesian framework for combining the predictions and different types of confidence scores from humans and machines.

Annual Review of Developmental Psychology***, 2020. [All Versions].

CogSci'11***, 2011. [All Versions]. [Preprint]. This paper presents a computational framework for understanding Theory of Mind (ToM): the human capacity for reasoning about agents’ mental states such as beliefs and desires. The proposed Bayesian model of ToM (or BToM) expresses the predictive model of belief- and desire-dependent action at the heart of ToM as a partially observable Markov decision process (POMDP), and reconstructs an agent’s joint belief state and reward function using Bayesian inference...

ICRA'05***, 2005. [All Versions].

Machine Learning***, 2021. [All Versions]. Given the recent successes of Deep Learning in AI there has been increased interest in the role and need for explanations in machine learned theories. A distinct notion in this context is that of Michie’s definition of ultra-strong machine learning (USML). USML is demonstrated by a measurable increase in human performance of a task following provision to the human of a symbolic machine learned theory for task performance. A recent paper demonstrates the benefici...

Journal of Experimental Psychology***, 2018. [All Versions]. There is a wealth of evidence that people’s reasoning is influenced by explanatory considerations. Three experiments investigate the descriptive adequacy of a precise proposal to be found in the philosophical literature, to wit, that we should infer to the best explanation, provided certain additional conditions are met. The main conslusions are that (a) the quality of an explanation is a good predictor of people’s willingness to accept that ex...

Artificial Intelligence***, 1999. [All Versions]. The original paper on operation reinforcement learning.

Science Robotics***, 2019. [All Versions]. Humans can infer concepts from image pairs and apply those in the physical world in a completely different setting, enabling tasks like IKEA assembly from diagrams. If robots could represent and infer high-level concepts, then it would notably improve their ability to understand our intent and to transfer tasks between different environments. To that end, the authors introduce a computational framework that replicates aspects of human concept learning. Concepts ...

Bioinformatics***, 2020. [All Versions]. Answering medical questions, identifying relevant clinical trials, and diagnosing diseases based on symptoms, making medical information more accessible to the general public.

Journal of Biological Engineering***, 2010. [All Versions]. [Project]. [Microsoft Page] This paper introduces BioCoder, a C++ library that enables biologists to express the exact steps needed to execute a protocol. In addition to being suitable for automation, BioCoder converts the code into a readable, English-language description for use by biologists.

Proceedings of the National Academy of Sciences***, 2021. [All Versions].

EMNLP'23***, 2023. [All Versions]. [[Project](https://github.com/bioplanner/bioplanner

NeurIPS'20***, 2020. [All Versions].

Cognition***, 2012. [All Versions].

Plato Stanford***. A computational philosophy account on Bounded Rationality, an elementary hypothesis of human intelligence in psychology and ecology.

CogSci'22***, 2022. [All Versions].

Current Directions in Psychological Science***, 2012. [All Versions]. A Marr's paradigm account on probabilistic models.

NeurIPS'19***, 2019. [All Versions]. [Slides]. [Code]. The original paper on Abductive Learning, a derivative-free approach for neuro-symbolic learning.

Nature Chemistry***, 2024. [All Versions]. This perspective article formulates eight principles to improve data management in scientific publications relating to data standardization, reproducibility and evaluation, and encourage scientists to go beyond current publication standards.

EMNLP'21***, 2021. [All Versions].

ACM Journal on Emerging Technologies in Computing Systems***, 2023. [All Versions]. Laboratory protocols are critical to biological research and development, yet difficult to communicate and reproduce across projects, investigators, and organizations. While many attempts have been made to address this challenge, there is currently no available protocol representation that is unambiguous enough for precise interpretation and automation, yet simultaneously “human friendly” and abstract enough to enable reu...

Behavioral and Brain Sciences***, 2017. [All Versions]. [Preprint]. Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human i...

Nature Human Behavior***, 2024. [All Versions]. [Preprint]. This perspective shows how the science of collaborative cognition can be put to work to engineer systems that really can be called ‘thought partners’, systems built to meet humans' expectations and complement humans' limitations. The authors lay out several modes of collaborative thought in which humans and artificial intelligence thought partners can engage, and they propose desiderata for human-compatible thought partnerships. Drawing on motif...

ICLR'20***, 2020. [Project].

ICML'21***, 2021. [All Versions].

Plato Stanford***. A computational philosophy account on Causal models, which are mathematical models representing causal relationships within an individual system or population.

The Oxford Handbook of Cognitive Psychology***, 2013. [All Versions].

Science***, 2006. [All Versions]. A piece of evidence for the capability of causal reasoning in intelligent animals.

Plato Stanford***. A computational philosophy account on causal theories of mental content, which attempts to explain how thoughts can be about things.

Wikipedia***. Wikipedia on causality, which is influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause.

, 2020.

Journal of the American Chemical Society (Au)***, 2021. [All Versions]. This paper describes a standard hardware (the chemical processing programming architecture --- the ChemPU) to encompass all chemical synthesis, an approach which unifies all chemistry automation strategies, from solid-phase peptide synthesis, to HTE flow chemistry platforms, while at the same time establishing a publication standard so that researchers can exchange chemical code (χDL) to ensure reproducibility and interoperability.

Philosophical Transactions of the Royal Society B: Biological Sciences***, 2020. [All Versions].

Psychological Review***, 1989. [All Versions]. A perspective against search as scientific thinking.

Child Development***, 2021. [All Versions].

Nature Communications***, 2021. [All Versions].

2023. [All Versions]. ChipNeMo aims to explore the applications of large language models (LLMs) for industrial chip design. Instead of directly deploying off-the-shelf commercial or open-source LLMs, the authors instead adopt the following domain adaptation techniques: domain-adaptive tokenization, domain-adaptive continued pretraining, model alignment with domain-specific instructions, and domain-adapted retrieval models. The authors evaluate these methods on three selected LLM applications for chip des...

IEEE Transactions on Visualization and Computer Graphics***, 2016. [All Versions].

NeurIPS'19***, 2019. [All Versions].

ICLR'20***, 2020. [All Versions].

ICML'20***, 2020. [All Versions].

CogSci'16***, 2016. [All Versions]. Models of concept learning and theory acquisition often invoke a stochastic search process, in which learners generate hypotheses through some structured random process and thenevaluate them on some data measuring their quality or value. To be successful within a reasonable time-frame, these models need ways of generating good candidate hypotheses evenbefore the data are considered. Schulz (2012a) has proposed that studying the origins of new ideas in more everyday con...

AAAI'20***, 2020. [All Versions].

EMNLP'20***, 2020. [All Versions]. Completing code, generating programming documentation, and providing technical support, making programming knowledge more accessible to non-experts.

Wikipedia***.

B.Blackwell***, 1991. [All Versions].

Human Factors***, 1988. [All Versions]. The original idea of investigating huamn tool use in problem solving.

CogSci'18***, 2018. [All Versions].

American Psychologist***, 1986. [All Versions]. Susan Carey's review on cognitive-science-based methodologies for science education research.

Annual Review of Control, Robotics, and Autonomous Systems***, 2022. [All Versions]. The review focuses on how cognitive science can provide forward models of human decision-making and inverse models of how humans think about others’ decision-making. The authors highlight relevant recent developments, including approaches that synthesize black box and theory-driven modeling, accounts that recast heuristics and biases as forms of bounded optimality, and models that characterize human theory of mind and co...

Annual Review of Psychology***, 1989. [All Versions].

ACL'19***, 2019.

Transactions of the Association for Computational Linguistics***, 2017. [All Versions].

POPL'23***, 2023. [All Versions]. A new algorithm that synthesizes functional reactive programs from observation data, which iterates between a functional synthesis step, which attempts to generate a transition function over observed states, and an automata synthesis step, which adds any additional latent state necessary to fully account for the observations.

AAAI'17***, 2017. [All Versions].

Plato Stanford***.

Artificial Intelligence***, 1984. [All Versions].

Communications of the ACM***, 2015. [All Versions]. Gary Marcus's review on commonsense knowledge in AI.

Proceedings of the National Academy of Sciences***, 2021. [All Versions]. Simulating the emergence of code as the communication bottleneck in color learning task.

NeurIPS'22***, 2022. [All Versions]. While humans readily generate and interpret instructions in a general language, computer systems are shackled to a narrow domain-specific language that they can precisely execute. This makes building intelligent systems that can generalize to novel situations such as ARC difficult. Human-generated instructions are referred as “natural programs”. While they resemble computer programs, they are distinct in two ways: First, they contain a wide range of primitives; Second...

Bioprocess and Biosystems Engineering***, 2016. [All Versions].

Wikipedia***. Programming languages may support multiple programming paradigms. This Wikipedia encyclopedia entry lists a concise reference for the programming paradigms.

IJCAI'20***, 2020. [All Versions].

Psychological Review***, 1972. [All Versions]. Herbert Simon's review on subjective complexity.

CogSci'92***, 1992. [All Versions]. Previous psychological research about scientific discovery has often focused on subjects' heuristics for discovering simple concepts with one relevant dimension or a few relevant dimensions with simple two-way interactions. This paper presents results from an experiment in which subjects had to discover a concept involving complex three-way interactions on a multi-valued output by running experiments in a computerized microworld. Twenty-two CMU undergraduates attempted...

NeurIPS'20***, 2020. [All Versions]. [Project]. A concept-composition version of network dissection.

MM'20***, 2020. [All Versions].

NeurIPS'20***, 2020. [All Versions].

Plato Stanford***. A computational philosophy account on compositionality, one of the distinctive feature of language.

ACL'20***, 2020. [All Versions].

Displays***, 2005. [All Versions]. Compressed file size, an objective, easily obtained measure of display complexity, predicts both subjective complexity judgments and objective search performance. It is analogous to algorithmic complexity, a theoretical but impractical measure of bit string complexity. The data suggest that it may be possible to use the compressed file size measure to predict display performance in applied tasks.

Proceedings of the National Academy of Sciences***, 2020. [All Versions]. A piece of evidence on hierarchical human planning.

Topics in Cognitive Science***, 2019. [All Versions]. This paper proposes an intuitive theory framework to studying affective cognition—how humans reason about emotions—and derive a taxonomy of inferences within affective cognition. Using this taxonomy, the authors review formal computational modeling work on such inferences, including causal reasoning about how others react to events, reasoning about unseen causes of emotions, reasoning with multiple cues, as well as reasoning from emotions to other men...

Science***, 2015. [All Versions]. A comprehensive review on the rationality of Bayesian computational models.

Topics in Cognitive Science***, 2014. [All Versions]. Introducing the computational rationality framework for including information-processing bounds in rational analyses, which emphasizes the incorporation of computational mechanism into the definition of rational action.

AAAI'17***, 2017. [All Versions]. Latest version of ConceptNet.

Plato Stanford***. A collection of the computational philosophical debates about the concepts.

Center for Brains, Minds, and Machines MEMO No.010***, 2014. [All Versions].

MIT Press***, 1985. [All Versions]. Susan Carey's book on the theory theory of concepts in child development.

International Journal of Computer Vision***, 2018. [All Versions]. [Preprint]. This work proposes a systematic learning-based approach to the generation of massive quantities of synthetic 3D scenes and arbitrary numbers of photorealistic 2D images thereof, with associated ground truth information, for the purposes of training, benchmarking, and diagnosing learning-based computer vision and robotics algorithms. In particular, the authors devise a learning-based pipeline of algorithms capable of automatica...

Psychological Review***, 1987. [All Versions]. A psychological account on hypothesis testing.

CogSci'21***, 2021. [All Versions].

CVPR'19***, 2019. [All Versions]. [Project]. Humans perceive the world using multi-modal sensory inputs such as vision, audition, and touch. This work investigates the cross-modal connection between vision and touch. The main challenge in this cross-domain modeling task lies in the significant scale discrepancy between the two: while our eyes perceive an entire visual scene at once, humans can only feel a small region of an object at any given moment. To connect vision and touch, this work introduces new...

ICML'17***, 2017. [All Versions]. The original paper on constrained reinforcement learning (safe reinforcement learning).

Journal of Experimental Psychology***, 1999. [All Versions]. [APA].

VIS-Gen4DS'24***, 2024. [All Versions]. [Preprint]. A position paper on DSL for data-driven storytelling. Data-driven storytelling serves as a crucial bridge for communicating ideas in a persuasive way. However, the manual creation of data stories is a multifaceted, labor-intensive, and case-specific effort, limiting their broader application. As a result, automating the creation of data stories has emerged as a significant research thrust. Despite advances in Artificial Intelligence, the systematic gene...

CogSci'15***, 2015. [All Versions].

CogSci'12***, 2012. [All Versions].

ICML'16***, 2016.

Nature Chemistry***, 2020. [All Versions]. [Preprint]. This paper shows how the Chemputer synthesis robot can be programmed to perform many different reactions, including solid-phase peptide synthesis, iterative cross-coupling and accessing reactive, unstable diazirines in a single, unified system with high yields and purity.

Cognition***, 1994. [All Versions].

IEEE Transactions on Pattern Analysis and Machine Intelligence***, 2018. [All Versions]. This paper studies the cooperative training of two generative models for image modeling and synthesis. Both models are parametrized by convolutional neural networks (ConvNets). The first model is a deep energy-based model, whose energy function is defined by a bottom-up ConvNet, which maps the observed image to the energy. We call it the descriptor network. The second model is a generator network, which is a non-line...

2021. [All Versions]. [Corel recipe page]. [International Network of Food Data Systems (INFOODS)]. The Corel DSL for cooking recipes enables understanding of and computation with ingredients, and can construct a nutrition label for the recipe.

NAACL'22***, 2022. [All Versions].

ICML'17***, 2017. [All Versions]. The original paper on curiosity as intrinsic motivation.

ICML'09***, 2009. [All Versions]. The original paper applying the idea of curriculum learning to machine learning.

iScience***, 2022. [All Versions]. This work proposes CX-ToM, short for counterfactual explanations with theory-of-mind, a new explainable AI (XAI) framework for explaining decisions made by a deep convolutional neural network (CNN). In contrast to the current methods in XAI that generate explanations as a single shot response, the authors pose explanation as an iterative communication process, i.e., dialogue between the machine and human user. More concretely, this CX-ToM framework generates a sequence ...

Communications of the ACM***, 1995. [All Versions]. The first attempt to build large-scale commonse knoweldgebase from human knowledge.

Engineering***, 2020. [All Versions]. Yixin Zhu and Song-Chun Zhu's review on visual commonsense.

Robotics and Automation Letters***, 2021. [All Versions].

IJCAI'17***, 2017. [All Versions]. [Project]. This paper proposes gcForest, a decision tree ensemble approach with performance highly competitive to deep neural networks in a broad range of tasks. In contrast to deep neural networks which require great effort in hyper-parameter tuning, gcForest is much easier to train; even when it is applied to different data across different domains in the experiments, excellent performance can be achieved by almost same settings of hyper-parameters. The training proce...

IEEE Information Theory Workshop'15***, 2015. [All Versions]. The first paper identifying the problem of information bottleneck in representation learning.

Cambridge University Press***, 2011. [All Versions].

NeurIPS'18***, 2018. [All Versions]. The original paper on neuro-symbolic probabilistic programming.

Philosophy of Science***, 1999. [All Versions].

IJCAI'16***, 2016. [All Versions].

CVPR'09***, 2009. [All Versions].

IROS'04***, 2004. [Project].

OOPSLA Workshop on Domain-Specific Modeling (DSM' 09)***, 2009. [All Versions]. Designing a new domain specific language is as any other complex task sometimes error-prone and usually time consuming, especially if the language shall be of high-quality and comfortably usable. Existing tool support focuses on the simplification of technical aspects but lacks support for an enforcement of principles for a good language design. In this paper we investigate guidelines that are useful for designing domain spec...

AAAI'18***, 2018.

Scientific Reports***, 2018. [All Versions]. The design of novel proteins has many applications but remains an attritional process with success in isolated cases. Meanwhile, deep learning technologies have exploded in popularity in recent years and are increasingly applicable to biology due to the rise in available data. This work attempts to link protein design and deep learning by using variational autoencoders to generate protein sequences conditioned on desired properties. Potential copper and calciu...

Communications of the ACM***, 2008. [All Versions].

Machine Learning***, 2022. [All Versions]. A neuro-symbolic framework that integrates meta-policy learning in inductive logic programming.

Children Development***, 2003. [All Versions].

CVPR'19***, 2019. [All Versions].

Plato Stanford***. A computational philosophy account on dialogical logic, which is a dialogue-based approach to logic and argumentation rooted in a research tradition that goes back to dialectics in Greek Antiquity, when problems were approached through dialogues in which opposing parties discussed a thesis through questions and answers.

Cerebral Cortex***, 2022. [All Versions].

Robotics: Science and Systems***, 2018. [All Versions].

Science***, 2022. [All Versions]. [Preprint]. This paper presents an automatically executable chemical reaction database of 100 molecules representative of the range of reactions found in contemporary organic synthesis. The chemical reaction codes or χDLs for the reactions have been stored in a database for version control, validation, collaboration, and data mining. Of these syntheses, more than 50 entries from the database have been downloaded and robotically run in seven modular chemputers with yields...

AAAI'22***, 2022. [All Versions].

NeurIPS'20***, 2020. [All Versions].

CogSci'19***, 2019. [All Versions].

New Generation Computing***, 2019. [All Versions].

NeurIPS'13***, 2013. [All Versions].

2020. [All Versions].

ACL'24***, 2024. [All Versions]. A preliminary work empirically showing that the intermediate embeddings of multilingual Transformers (1) start far away from output token embeddings; (2) already allow for decoding a semantically correct next token in the middle layers, but give higher probability to its version in English than in the input language; (3) finally move into an input-language-specific region of the embedding space. Also, the embedding of abstract concept space lies closer to English than to ...

Proceedings of the Royal Society B: Biological Sciences***, 2009. [All Versions]. A piece of evidence for the capability of causal reasoning in intelligent animals.

Computational Brain & Behavior***, 2018. [All Versions].

Cognition***, 1987. [All Versions].

Wikipedia***. Wikipedia encyclopedia entry on Domain Engineering.

ICBC'20***, 2020. [All Versions]. [Preprint]. This research addresses the understanding hardness raised from the conceptual discrepancy between contractual clauses and corresponding code of the Solidity programming language, by the design and study of a domain-specific smart contract language based on higher level of abstraction that can be automatically transformed to an implementation.

Wikipedia***. Wikipedia encyclopedia entry on Domain Specific Languages.

Pearson Education***, 2010. [All Versions]. [Domain-Specific Languages Guide]. When carefully selected and used, Domain-Specific Languages (DSLs) may simplify complex code, promote effective communication with customers, improve productivity, and unclog development bottlenecks. In Domain-Specific Languages, noted software development expert Martin Fowler first provides the information software professionals need to decide if and when to utilize DSLs. Then, where DSLs prove suitable, Fowler presents effec...