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Modeling Social Causality and Responsibility Judgment in Multi-Agent Interactions
"... Social causality is the inference an entity makes about the social behavior of other entities and self. Besides physical cause and effect, social causality involves reasoning about epistemic states of agents and coercive circumstances. Based on such inference, responsibility judgment is the process ..."
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Social causality is the inference an entity makes about the social behavior of other entities and self. Besides physical cause and effect, social causality involves reasoning about epistemic states of agents and coercive circumstances. Based on such inference, responsibility judgment is the process whereby one singles out individuals to assign responsibility, credit or blame for multiagent activities. Social causality and responsibility judgment are a key aspect of social intelligence, and a model for them facilitates the design and development of a variety of multiagent interactive systems. Based on psychological attribution theory, this paper presents a domain-independent computational model to automate social inference and judgment process according to an agent’s causal knowledge and observations of interaction. We conduct experimental studies to empirically validate the computational model. The experimental results show that our model predicts human judgments of social attributions and makes inferences consistent with what most people do in their judgments. Therefore, the proposed model can be generically incorporated into an intelligent system to augment its social and cognitive functionality. 1.
A Cognitive Model of Recognition-Based Moral Decision Making
, 2009
"... The study of decision making has been dominated by economic perspectives, which model people as rational agents who carefully weigh costs and benefits and try to maximize the utility of every choice, without consideration of issues such as cultural norms, religious beliefs and moral rules. However, ..."
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The study of decision making has been dominated by economic perspectives, which model people as rational agents who carefully weigh costs and benefits and try to maximize the utility of every choice, without consideration of issues such as cultural norms, religious beliefs and moral rules. However, psychological findings indicate that in many situations people are not rational decision makers as defined by the economic theories. One of the domains in which traditional cost-benefit models fail to predict human behavior is the domain of moral reasoning. This work presents the first computational model of recognitionbased moral decision making, MoralDM, which integrates several AI techniques in order to model recent psychological findings on moral decision making. MoralDM uses a natural language system to produce formal representations from psychological stimuli,
Using Narrative Function to Extract Qualitative Information from Natural Language Texts: A Preliminary Report �
"... The naturalness of qualitative reasoning suggests that qualitative representations might be an important component of the semantics of natural language. Prior work ( Kuehne 2004) showed that frame-based representations of qualitative process theory constructs could indeed be extracted from natural l ..."
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The naturalness of qualitative reasoning suggests that qualitative representations might be an important component of the semantics of natural language. Prior work ( Kuehne 2004) showed that frame-based representations of qualitative process theory constructs could indeed be extracted from natural language texts. Kuehne’s approach relied on the parser recognizing specific syntactic constructions, which has limited coverage. This paper describes a new approach, using narrative function to represent the higher-order relationships between the constituents of a sentence and between sentences in a discourse. We outline how narrative function combined with query-driven abduction enables the same kinds of information to be extracted from natural language texts. Moreover, we also show how type-level qualitative representations (Hinrichs & Forbus, 2012) can be extracted from text, and used to improve performance in playing a strategy game. 1
ABSTRACT Language Understanding by Reference Resolution in Episodic Memory
, 2009
"... This dissertation presents an approach to language understanding that treats all ambiguity resolution as a problem of reference resolution: grounding references to episodic memory. This model of language understanding is evaluated with an implementation of DMAP (Direct Memory Access Parsing) called ..."
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This dissertation presents an approach to language understanding that treats all ambiguity resolution as a problem of reference resolution: grounding references to episodic memory. This model of language understanding is evaluated with an implementation of DMAP (Direct Memory Access Parsing) called REDMAP (Reference resolution in Episodic memory for DMAP). DMAP is a language understanding model that recognizes its input by mapping phrasal patterns to existing knowledge structures, updating memory with new information only as needed. REDMAP works with a large logic based memory (evaluated with ResearchCyc 1.28 million assertions). It uses lexically driven rules to form candidate sets of assertions, and queries memory to ground references in those assertions to existing instances. Assertions from subsequent sentences are merged with running interpretations by identifying how new references are mapped to existing references. Mappings are evaluated by propagating remindings to existing instances to the new references. These instances are substituted into the new assertions, and memory is queried for their existence. If found these assertions support the reference mapping. Additionally, these queries will simultaneously ground any new unmapped references,
Moral Decision-Making by Analogy: Generalizations vs. Exemplars
"... Moral reasoning is important to model accurately as AI systems become ever more integrated into our lives. Moral reasoning is rapid and unconscious; analogical reasoning, which can be unconscious, is a promising approach to model moral reasoning. This paper explores the use of analogical generalizat ..."
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Moral reasoning is important to model accurately as AI systems become ever more integrated into our lives. Moral reasoning is rapid and unconscious; analogical reasoning, which can be unconscious, is a promising approach to model moral reasoning. This paper explores the use of analogical generalizations to improve moral reasoning. Analogical reasoning has already been used successfully to model moral reasoning in the MoralDM model, but it exhaustively matches across all known cases, which is computationally intractable and cognitively implausible for human-scale knowledge bases. We investigate the performance of an extension of MoralDM to use the MAC/FAC model of analogical retrieval over three conditions, across a set of highly confusable moral scenarios.
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"... The naturalness of qualitative reasoning suggests that qualitative representations might be an important component of the semantics of natural language. Prior work ( Kuehne 2004) showed that frame-based representations of qualitative process theory constructs could indeed be extracted from natural l ..."
Abstract
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The naturalness of qualitative reasoning suggests that qualitative representations might be an important component of the semantics of natural language. Prior work ( Kuehne 2004) showed that frame-based representations of qualitative process theory constructs could indeed be extracted from natural language texts. Kuehne’s approach relied on the parser recognizing specific syntactic constructions, which has limited coverage. This paper describes a new approach, using narrative function to represent the higher-order relationships between the constituents of a sentence and between sentences in a discourse. We outline how narrative function combined with query-driven abduction enables the same kinds of information to be extracted from natural language texts. Moreover, we also show how type-level qualitative representations (Hinrichs & Forbus, 2012) can be extracted from text, and used to improve performance in playing a strategy game. 1
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, 2009
"... The study of decision making has been dominated by economic perspectives, which model people as rational agents who carefully weigh costs and benefits and try to maximize the utility of every choice, without consideration of issues such as cultural norms, religious beliefs and moral rules. However, ..."
Abstract
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(Show Context)
The study of decision making has been dominated by economic perspectives, which model people as rational agents who carefully weigh costs and benefits and try to maximize the utility of every choice, without consideration of issues such as cultural norms, religious beliefs and moral rules. However, psychological findings indicate that in many situations people are not rational decision makers as defined by the economic theories. One of the domains in which traditional cost-benefit models fail to predict human behavior is the domain of moral reasoning. This work presents the first computational model of recognition-based moral decision making, MoralDM, which integrates several AI techniques in order to model recent psychological findings on moral decision making. MoralDM uses a natural language system to produce formal representations from psychological stimuli, reducing tailorability. The impacts of secular versus sacred values are modeled via qualitative reasoning, using an order of magnitude representation. MoralDM uses a combination of first-principles reasoning and analogical reasoning to model the recognition-based mode of decision making. The results of MoralDM experiments provided the impetus to further examine the role of cultural narratives and analogical reasoning on moral decision making. This work examines
