More Like this

1. An Answer Set Programming Framework for Reasoning about Agents’ Beliefs and Truthfulness of Statements

   Balduccini, Marcello ; Gelfond, Michael ; Pontelli, Enrico ; Son, Tran C ( September 2020 , International Conference on Principles of Knowledge Representation and Reasoning (KR 2020))

   null (Ed.)

   The paper proposes a framework for capturing how an agent’s beliefs evolve over time in response to observations and for answering the question of whether statements made by a third party can be believed. The basic components of the framework are a formalism for reasoning about actions, changes, and observations and a formalism for default reasoning. 

   more » « less
2. A Logic Programming Approach to Regression-Based Repair of Incorrect Initial Belief States

   https://doi.org/10.1007/978-3-030-67438-0_5  

   Tardivo, F. ( January 2021 , Lecture notes in computer science)

   This paper explores the challenge of encountering incorrect beliefs in the context of reasoning about actions and changes using action languages with sensing actions. An incorrect belief occurs when some observations conflict with the agent’s own beliefs. A common approach to recover from this situation is to replace the initial beliefs with beliefs that conform to the sequence of actions and the observations. The paper introduces a regression-based and revision-based approach to calculate a correct initial belief. Starting from an inconsistent history consisting of actions and observations, the proposed framework (1) computes the initial belief states that support the actions and observations and (2) uses a belief revision operator to repair the false initial belief state. The framework operates on domains with static causal laws, supports arbitrary sequences of actions, and integrates belief revision methods to select a meaningful initial belief state among possible alternatives. 

   more » « less
3. An Action Language for Multi-Agent Domains: Foundations

   Baral, C ; Gelfond, G ; Pontelli, E ; Son, T ( December 2019 , Artificial intelligence)

   In multi-agent domains (MADs), an agent's action may not just change the world and the agent's knowledge and beliefs about the world, but also may change other agents' knowledge and beliefs about the world and their knowledge and beliefs about other agents' knowledge and beliefs about the world. The goals of an agent in a multi-agent world may involve manipulating the knowledge and beliefs of other agents' and again, not just their knowledge/belief about the world, but also their knowledge about other agents' knowledge about the world. Our goal is to present an action language (mA+) that has the necessary features to address the above aspects in representing and RAC in MADs. mA+ allows the representation of and reasoning about different types of actions that an agent can perform in a domain where many other agents might be present -- such as world-altering actions, sensing actions, and announcement/communication actions. It also allows the specification of agents' dynamic awareness of action occurrences which has future implications on what agents' know about the world and other agents' knowledge about the world. mA+ considers three different types of awareness: full-, partial- awareness, and complete oblivion of an action occurrence and its effects. This keeps the language simple, yet powerful enough to address a large variety of knowledge manipulation scenarios in MADs. The semantics of mA+ relies on the notion of state, which is described by a pointed Kripke model and is used to encode the agent's knowledge and the real state of the world. It is defined by a transition function that maps pairs of actions and states into sets of states. We illustrate properties of the action theories, including properties that guarantee finiteness of the set of initial states and their practical implementability. Finally, we relate mA+ to other related formalisms that contribute to RAC in MADs. 

   more » « less
4. FOOLING MYSELF OR FOOLING OBSERVERS? AVOIDING SOCIAL PRESSURES BY MANIPULATING PERCEPTIONS OF DESERVINGNESS OF OTHERS

   https://doi.org/10.1111/ecin.12777  

   Andreoni, James ; Sanchez, Alison ( April 2019 , Economic Inquiry)

   We present a novel experiment demonstrating strategies selfish individuals utilize to avoid social pressure to be altruistic. Subjects participate in a trust game, after which they have an opportunity to state their beliefs about their opponent's actions. Subsequently, subjects participate in a task designed to “reveal” their true beliefs. Subjects who initially made selfish choices falsely state their beliefs about their opponent's kindness. Their “revealed” beliefs were significantly more accurate, which exposed subjects' knowledge that their selfishness was unjustifiable by their opponent's behavior. The initial false statements complied with social norms, suggesting subjects' attempts to project a more favorable social image. (JELC9, D03, D83)

    

   more » « less
5. Building representations of the social world: Children extract patterns from social choices to reason about multi‐group hierarchies

   https://doi.org/10.1111/desc.13366  

   Heck, Isobel A. ; Kushnir, Tamar ; Kinzler, Katherine D. ( February 2023 , Developmental Science)

   How do children learn about the structure of the social world? We tested whether children would extract patterns from an agent's social choices to make inferences about multiple groups’ relative social standing. In Experiment 1, 4‐ to 6‐year‐old children (N = 36; tested in Central New York) saw an agent and three groups (Group‐A,Group‐B, andGroup‐C) and observed the agent choose between pairs of individuals from different groups. Across pairwise selections, a pattern emerged: The agent chose individuals fromGroup‐A > Group‐B > Group‐C. Children tracked the agent's choices to predict thatGroup‐Awas “most‐preferred” and the “leader” and thatGroup‐Cwas “least‐preferred” and the “helper.” In Experiments 2 and 3, we examined children's reasoning about a more complex pattern involving four groups and tested a wider age range. In Experiment 2, 5‐ to 10‐year‐old children (N = 98; tested in Central New York) used the agent's pattern of pairwise choices to infer that the agent likedGroup‐A > Group‐B > Group‐C > Group‐Dand to make predictions about which groups were likely to be “leaders” and “helpers.” In Experiment 3, we found evidence for social specificity in children's reasoning: 5‐ to 10‐year‐old children (N = 96; from 26 US States) made inferences about groups’ relativesocialbut notphysicalpower from the agent's pattern of affiliative choices across the four groups. These findings showcase a mechanism through which children may learn about societal‐level hierarchies through the patterns they observe over time in people's group‐based social choices.

   Children in our sample extracted patterns from an agent's positive social choices between multiple groups to reason about groups’ relative social standing.

   Children used the pattern of an agent's positive social choices to guide their reasoning about which groups were likely to be “leaders” and “helpers” in a fictional town.

   The pattern that emerged in an agent's choices of friends shaped children's thinking about groups’ relativesocialbut notphysicalpower.

   Children tracked social choices to reason about group‐based hierarchies at the individual level (which groups an agent prefers) and societal level (which groups are privileged).

    

   more » « less