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Andrew S. Gordon - One of the best experts on this subject based on the ideXlab platform.
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A Formal Theory of Commonsense Psychology: How People Think People Think
2017Co-Authors: Andrew S. Gordon, Jerry R. HobbsAbstract:Commonsense Psychology refers to the implicit theories that we all use to make sense of people's behavior in terms of their beliefs, goals, plans, and emotions. These are also the theories we employ when we anthropomorphize complex machines and computers as if they had humanlike mental lives. In order to successfully cooperate and communicate with people, these theories will need to be represented explicitly in future artificial intelligence systems. This book provides a large-scale logical formalization of Commonsense Psychology in support of humanlike artificial intelligence. It uses formal logic to encode the deep lexical semantics of the full breadth of psychological words and phrases, providing fourteen hundred axioms of first-order logic organized into twenty-nine Commonsense Psychology theories and sixteen background theories. This in-depth exploration of human Commonsense reasoning for artificial intelligence researchers, linguists, and cognitive and social psychologists will serve as a foundation for the development of humanlike artificial intelligence.
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AAAI Spring Symposia - One Hundred Challenge Problems for Logical Formalizations of Commonsense Psychology.
2015Co-Authors: Nicole Maslan, Melissa Roemmele, Andrew S. GordonAbstract:We present a new set of challenge problems for the logical formalization of Commonsense knowledge, called TriangleCOPA. This set of one hundred problems is smaller than other recent Commonsense reasoning question sets, but is unique in that it is specifically designed to support the development of logic-based Commonsense theories, via two means. First, questions and potential answers are encoded in logical form using a fixed vocabulary of predicates, eliminating the need for sophisticated natural language processing pipelines. Second, the domain of the questions is tightly constrained so as to focus formalization efforts on one area of inference, namely the Commonsense reasoning that people do about human Psychology. We describe the authoring methodology used to create this problem set, and our analysis of the scope of requisite Commonsense knowledge. We then show an example of how problems can be solved using an implementation of weighted abduction.
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one hundred challenge problems for logical formalizations of Commonsense Psychology
National Conference on Artificial Intelligence, 2015Co-Authors: Nicole Maslan, Melissa Roemmele, Andrew S. GordonAbstract:We present a new set of challenge problems for the logical formalization of Commonsense knowledge, called TriangleCOPA. This set of one hundred problems is smaller than other recent Commonsense reasoning question sets, but is unique in that it is specifically designed to support the development of logic-based Commonsense theories, via two means. First, questions and potential answers are encoded in logical form using a fixed vocabulary of predicates, eliminating the need for sophisticated natural language processing pipelines. Second, the domain of the questions is tightly constrained so as to focus formalization efforts on one area of inference, namely the Commonsense reasoning that people do about human Psychology. We describe the authoring methodology used to create this problem set, and our analysis of the scope of requisite Commonsense knowledge. We then show an example of how problems can be solved using an implementation of weighted abduction.
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Toward a Large-Scale Formal Theory of Commonsense Psychology for Metacognition
2014Co-Authors: Jerry R. Hobbs, Andrew S. GordonAbstract:Robust intelligent systems will require a capacity for metacognitive reasoning, where intelligent systems monitor and reflect on their own reasoning processes. A large-scale study of human strategic reasoning indicates that rich representational models of Commonsense Psychology are available to enable human metacognition. In this paper, we argue that large-scale formalizations of Commonsense Psychology enable metacognitive reasoning in intelligent systems. We describe our progress toward developing 30 integrated axiomatic theories of Commonsense Psychology, and discuss the central representational challenges that have arisen in this work to date. Commonsense Psychology and Metacognitive Reasonin
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Commonsense Psychology and the Functional Requirements of Cognitive Models
2014Co-Authors: Andrew S. GordonAbstract:In this paper we argue that previous models of cognitive abilities (e.g. memory, analogy) have been constructed to satisfy functional requirements of implicit Commonsense psychological theories held by researchers and non-researchers alike. Rather than working to avoid the influence of Commonsense Psychology in cognitive modeling research, we propose to capitalize on progress in developing formal theories of Commonsense Psychology to explicitly define the functional requirements of cognitive models. We present a taxonomy of 16 classes of cognitive models that correspond to the representational areas that have been addressed in large-scale inferential theories of Commonsense Psychology. We consider the functional requirements that can be derived from inferential theorie
Joshua B. Tenenbaum - One of the best experts on this subject based on the ideXlab platform.
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Social Pragmatics: Preschoolers Rely on Commonsense Psychology to Resolve Referential Underspecification.
Child development, 2019Co-Authors: Julian Jara-ettinger, Sammy Floyd, Holly Huey, Joshua B. Tenenbaum, Laura SchulzAbstract:Four experiments show that 4- and 5-year-olds (total N = 112) can identify the referent of underdetermined utterances through their Naive Utility Calculus?an intuitive theory of people?s behavior structured around an assumption that agents maximize utilities. In Experiments 1?2, a puppet asked for help without specifying to whom she was talking (?Can you help me??). In Experiments 3?4, a puppet asked the child to pass an object without specifying what she wanted (?Can you pass me that one??). Children?s responses suggest that they considered cost trade-offs between the members in the interaction. These findings add to a body of work showing that reference resolution is informed by Commonsense Psychology from early in childhood.
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The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology: (Trends in Cognitive Sciences 20, 589-604; July 19, 2016).
Trends in cognitive sciences, 2016Co-Authors: Julian Jara-ettinger, Laura Schulz, Hyowon Gweon, Joshua B. TenenbaumAbstract:Due to an oversight in the preparation of this Feature Review article, the authors mistakenly labeled Figure 2 Panel E “Forego low-cost and high-cost plans”. The correct label for Figure 2 Panel E is “Forego low-reward and high-reward plans”. Figure 2 has been corrected in the article online. The correct version of the panel is also shown here.View Large Image | Download PowerPoint Slide
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The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology
Trends in cognitive sciences, 2016Co-Authors: Julian Jara-ettinger, Laura Schulz, Hyowon Gweon, Joshua B. TenenbaumAbstract:We propose that human social cognition is structured around a basic understanding of ourselves and others as intuitive utility maximizers: from a young age, humans implicitly assume that agents choose goals and actions to maximize the rewards they expect to obtain relative to the costs they expect to incur. This 'naive utility calculus' allows both children and adults observe the behavior of others and infer their beliefs and desires, their longer-term knowledge and preferences, and even their character: who is knowledgeable or competent, who is praiseworthy or blameworthy, who is friendly, indifferent, or an enemy. We review studies providing support for the naive utility calculus, and we show how it captures much of the rich social reasoning humans engage in from infancy.
Laura Schulz - One of the best experts on this subject based on the ideXlab platform.
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Social Pragmatics: Preschoolers Rely on Commonsense Psychology to Resolve Referential Underspecification.
Child development, 2019Co-Authors: Julian Jara-ettinger, Sammy Floyd, Holly Huey, Joshua B. Tenenbaum, Laura SchulzAbstract:Four experiments show that 4- and 5-year-olds (total N = 112) can identify the referent of underdetermined utterances through their Naive Utility Calculus?an intuitive theory of people?s behavior structured around an assumption that agents maximize utilities. In Experiments 1?2, a puppet asked for help without specifying to whom she was talking (?Can you help me??). In Experiments 3?4, a puppet asked the child to pass an object without specifying what she wanted (?Can you pass me that one??). Children?s responses suggest that they considered cost trade-offs between the members in the interaction. These findings add to a body of work showing that reference resolution is informed by Commonsense Psychology from early in childhood.
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The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology: (Trends in Cognitive Sciences 20, 589-604; July 19, 2016).
Trends in cognitive sciences, 2016Co-Authors: Julian Jara-ettinger, Laura Schulz, Hyowon Gweon, Joshua B. TenenbaumAbstract:Due to an oversight in the preparation of this Feature Review article, the authors mistakenly labeled Figure 2 Panel E “Forego low-cost and high-cost plans”. The correct label for Figure 2 Panel E is “Forego low-reward and high-reward plans”. Figure 2 has been corrected in the article online. The correct version of the panel is also shown here.View Large Image | Download PowerPoint Slide
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The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology
Trends in cognitive sciences, 2016Co-Authors: Julian Jara-ettinger, Laura Schulz, Hyowon Gweon, Joshua B. TenenbaumAbstract:We propose that human social cognition is structured around a basic understanding of ourselves and others as intuitive utility maximizers: from a young age, humans implicitly assume that agents choose goals and actions to maximize the rewards they expect to obtain relative to the costs they expect to incur. This 'naive utility calculus' allows both children and adults observe the behavior of others and infer their beliefs and desires, their longer-term knowledge and preferences, and even their character: who is knowledgeable or competent, who is praiseworthy or blameworthy, who is friendly, indifferent, or an enemy. We review studies providing support for the naive utility calculus, and we show how it captures much of the rich social reasoning humans engage in from infancy.
Julian Jara-ettinger - One of the best experts on this subject based on the ideXlab platform.
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Social Pragmatics: Preschoolers Rely on Commonsense Psychology to Resolve Referential Underspecification.
Child development, 2019Co-Authors: Julian Jara-ettinger, Sammy Floyd, Holly Huey, Joshua B. Tenenbaum, Laura SchulzAbstract:Four experiments show that 4- and 5-year-olds (total N = 112) can identify the referent of underdetermined utterances through their Naive Utility Calculus?an intuitive theory of people?s behavior structured around an assumption that agents maximize utilities. In Experiments 1?2, a puppet asked for help without specifying to whom she was talking (?Can you help me??). In Experiments 3?4, a puppet asked the child to pass an object without specifying what she wanted (?Can you pass me that one??). Children?s responses suggest that they considered cost trade-offs between the members in the interaction. These findings add to a body of work showing that reference resolution is informed by Commonsense Psychology from early in childhood.
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The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology: (Trends in Cognitive Sciences 20, 589-604; July 19, 2016).
Trends in cognitive sciences, 2016Co-Authors: Julian Jara-ettinger, Laura Schulz, Hyowon Gweon, Joshua B. TenenbaumAbstract:Due to an oversight in the preparation of this Feature Review article, the authors mistakenly labeled Figure 2 Panel E “Forego low-cost and high-cost plans”. The correct label for Figure 2 Panel E is “Forego low-reward and high-reward plans”. Figure 2 has been corrected in the article online. The correct version of the panel is also shown here.View Large Image | Download PowerPoint Slide
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The Naïve Utility Calculus: Computational Principles Underlying Commonsense Psychology
Trends in cognitive sciences, 2016Co-Authors: Julian Jara-ettinger, Laura Schulz, Hyowon Gweon, Joshua B. TenenbaumAbstract:We propose that human social cognition is structured around a basic understanding of ourselves and others as intuitive utility maximizers: from a young age, humans implicitly assume that agents choose goals and actions to maximize the rewards they expect to obtain relative to the costs they expect to incur. This 'naive utility calculus' allows both children and adults observe the behavior of others and infer their beliefs and desires, their longer-term knowledge and preferences, and even their character: who is knowledgeable or competent, who is praiseworthy or blameworthy, who is friendly, indifferent, or an enemy. We review studies providing support for the naive utility calculus, and we show how it captures much of the rich social reasoning humans engage in from infancy.
Jerry R. Hobbs - One of the best experts on this subject based on the ideXlab platform.
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A Formal Theory of Commonsense Psychology: How People Think People Think
2017Co-Authors: Andrew S. Gordon, Jerry R. HobbsAbstract:Commonsense Psychology refers to the implicit theories that we all use to make sense of people's behavior in terms of their beliefs, goals, plans, and emotions. These are also the theories we employ when we anthropomorphize complex machines and computers as if they had humanlike mental lives. In order to successfully cooperate and communicate with people, these theories will need to be represented explicitly in future artificial intelligence systems. This book provides a large-scale logical formalization of Commonsense Psychology in support of humanlike artificial intelligence. It uses formal logic to encode the deep lexical semantics of the full breadth of psychological words and phrases, providing fourteen hundred axioms of first-order logic organized into twenty-nine Commonsense Psychology theories and sixteen background theories. This in-depth exploration of human Commonsense reasoning for artificial intelligence researchers, linguists, and cognitive and social psychologists will serve as a foundation for the development of humanlike artificial intelligence.
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Toward a Large-Scale Formal Theory of Commonsense Psychology for Metacognition
2014Co-Authors: Jerry R. Hobbs, Andrew S. GordonAbstract:Robust intelligent systems will require a capacity for metacognitive reasoning, where intelligent systems monitor and reflect on their own reasoning processes. A large-scale study of human strategic reasoning indicates that rich representational models of Commonsense Psychology are available to enable human metacognition. In this paper, we argue that large-scale formalizations of Commonsense Psychology enable metacognitive reasoning in intelligent systems. We describe our progress toward developing 30 integrated axiomatic theories of Commonsense Psychology, and discuss the central representational challenges that have arisen in this work to date. Commonsense Psychology and Metacognitive Reasonin
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Abduction of Mental States with a Formal Theory of Commonsense Psychology
2013Co-Authors: Andrew S. Gordon, Jerry R. Hobbs, Katya Ovchinnikova, Melissa Roemmele, Louis-philippe MorencyAbstract:Successful communication and collaboration between humans and intelligent agents of the future will require a robust ability to algorithmically infer the subjective mental states of the human participants. As in human to human interaction, the central concerns of plans, goals, emotions, and beliefs of another must inferred from a mix of explicit and implicit evidence in language, along with contextual and behavioral cues. We propose that this cognitive ability of mental model ascription is best conceived as a process of abduction, where a hypothetical explanation is inferred to account for observable evidence. In this approach, speech and other behavior of a person are observables that require explanation, where the challenge is to find a theoretical explanation that requires the fewest assumptions. Recent advances in abduction-based language processing [1] have led to efficient implementations of Hobbs's conception of weighted-abduction [2], where textual inputs (observations) are explained by searching a knowledgebase of logical axioms for the least-cost proof, with cost incurred when assumptions are asserted. For mental model ascription, the knowledgebase of axioms used to explain the observable behavior of others would constitute a Theory of Mind, a set of inference rules that encode a Commonsense understanding of human Psychology. In our own work [3], we attempt to formalize a large-coverage theory of Commonsense Psychology in firstorder predicate logic. Our formalization efforts have been organized around 30 specific content theories of various mental states and processes, including those related to plans, goals, emotions, beliefs, decisions, explanations, and expectations. We hypothesize that the contents of these formal theories are sufficiently rich to serve as a theoretical foundation for mental model ascription, and are now working to integrate these theories into an abduction-based interpretation system. To explore this hypothesis, we have chosen to focus our initial efforts not on the interpretation of language evidence, but rather on motion and gesture observations. To further simplify the task of recognizing low-level motion and gesture actions, we are building a system that ascribes mental models to abstract shapes moving around an empty field of view in the style of the stimulus used in Heider and Simmel's classic experiment on intention perception [4]. In this ongoing project, our aim is to produce a computational model of mental state attribution from the observable actions of others, and build a foundation for a broader model that incorporates additional evidence including language.
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The Deep Lexical Semantics of Emotions
Affective Computing and Sentiment Analysis, 2011Co-Authors: Jerry R. Hobbs, Andrew S. GordonAbstract:The research described here is part of a larger effort, first, to construct formal theories of a broad range of aspects of Commonsense Psychology, including knowledge management, the envisionment of possible courses of events, and goal-directed behavior, and, second, to link them to the English lexicon. We have identified the most common words and phrases for describing emotions in English. In this paper we describe a formalization of people’s implicit theory of how emotions mediate between what they experience and what they do. We then sketch out effort to write rules that link the theory with words and phrases in the emotional lexicon.
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Metareasoning - Anthropomorphic self-models for metareasoning agents
2011Co-Authors: Andrew S. Gordon, Jerry R. Hobbs, Michael CoxAbstract:Representations of an AI agent’s mental states and processes are necessary to enable metareasoning, i.e. thinking about thinking. However, the formulation of suitable representations remains an outstanding AI research challenge, with no clear consensus on how to proceed. This paper outlines an approach involving the formulation of anthropomorphic self-models, where the representations that are used for metareasoning are based on formalizations of Commonsense Psychology. We describe two research activities that support this approach, the formalization of broad-coverage Commonsense Psychology theories and use of representations in the monitoring and control of objectlevel reasoning. We focus specifically on metareasoning about memory, but argue that anthropomorphic self-models support the development of integrated, reusable, broadcoverage representations for use in metareasoning systems. Self-models in Metareasoning Cox and Raja (2007) define reasoning as a decision cycle within an action-perception loop between the ground level (doing) and the object level (reasoning). Metareasoning is further defined as a second loop, where this reasoning is itself monitored and controlled in order to improve the quality of the reasoning decisions that are made (Figure 1). Figure 1. Multi-level model of reasoning It has long been recognized (e.g., McCarthy, 1958) that to better understand and act upon the environment, an agent should have an explicit, declarative representation of the states and actions occurring in that environment. Thus the task at the object level is to create a declarative model of the world and to use such a representation to facilitate the selection of actions at the ground level. It follows also that to reason about other agents in the world (e.g., to Copyright © 2008, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. anticipate what they may do in the future), it helps to have a representation of the agents in the world, what they know, and how they think. Likewise an explicit representation of the self supports reasoning about oneself and hence facilitates metareasoning. Representations provide structure and enable inference. They package together related assertions so that knowledge is organized and brought to bear effectively and efficiently. One of the central concerns in the model of metareasoning as shown in Figure 1 is the character of the information that is passed between the object level and the meta-level reasoning modules to enable monitoring and control. Cast as a representation problem, the question becomes: How should an agent’s own reasoning be represented to itself as it monitors and controls this reasoning? Cox and Raja (2007) describe these representations as models of self, which serve to control an agent’s reasoning choices, represent the product of monitoring, and coordinate the self in social contexts. Self-models have been periodically explored in previous AI research since Minsky (1968), and explicit self-models have been articulated for a diverse set of reasoning processes that include threat detection (Birnbaum et al., 1990), case retrieval (Fox & Leake, 1995), and expectation management (Cox, 1997). Typically built to demonstrate a limited metareasoning capacity, these self-models have lacked several qualities that should be sought in future research in this area, including: 1. Broad coverage: Self-models should allow an agent to reason about and control the full breadth of their object-level reasoning processes. 2. Integrated: Self-models of different reasoning processes should be compatible with one another, allowing an agent to reason about and control the interaction between different reasoning subsystems. 3. Reusable: The formulation of self-models across different agents and agent architectures should have some commonalities that allow developers to apply previous research findings when building new systems. Despite continuing interest in metareasoning over the last two decades (see Anderson & Oates, 2007; Cox, 2005), there has been only modest progress toward the development of self-models that achieve these desirable qualities. We speculate that this is due, in part, to an