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1. 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. 

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2. An Answer Set Programming Framework for Reasoning about Agents' Beliefs and Truthfulness of Statements

   https://doi.org/10.24963/kr.2020/8  

   Balduccini, Marcello; Gelfond, Michael; Pontelli, Enrico; Son, Tran Cao (July 2020, Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning)

   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. The paper describes a concrete implementation that leverages answer set programming for determining the evolution of an agent's ``belief state'', based on observations, knowledge about the effects of actions, and a theory about how these influence an agent's beliefs. The beliefs are then used to assess whether statements made by a third party can be accepted as truthful. The paper investigates an application of the proposed framework in the detection of man-in-the-middle attacks targeting computers and cyber-physical systems. Finally, we briefly discuss related work and possible extensions. 

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3. Combining Intentionality and Belief: Revisiting Believable Character Plans

   Shirvani, Alireza; Farrell, Rachelyn; Ware, Stephen G. (October 2018, Proceedings of the 14th AAAI International Conference on Artificial Intelligence and Interactive Digital Entertainment)

   In this paper we present two studies supporting a plan-based model of narrative generation that reasons about both intentionality and belief. First we compare the believability of agent plans taken from the spaces of valid classical plans, intentional plans, and belief plans. We show that the plans that make the most sense to humans are those in the overlapping regions of the intentionality and belief spaces. Second, we validate the model’s approach to representing anticipation, where characters form plans that involve actions they expect other characters to take. Using a short interactive scenario we demonstrate that players not only find it believable when NPCs anticipate their actions, but sometimes actively anticipate the actions of NPCs in a way that is consistent with the model. 

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4. Integrated Task and Motion Planning for Safe Legged Navigation in Partially Observable Environments

   https://doi.org/10.1109/TRO.2023.3299524  

   Shamsah, Abdulaziz; Gu, Zhaoyuan; Warnke, Jonas; Hutchinson, Seth; Zhao, Ye (August 2023, IEEE Transactions on Robotics)

   This study proposes a hierarchically integrated framework for safe task and motion planning (TAMP) of bipedal locomotion in a partially observable environment with dynamic obstacles and uneven terrain. The high-level task planner employs linear temporal logic for a reactive game synthesis between the robot and its environment and provides a formal guarantee on navigation safety and task completion. To address environmental partial observability, a belief abstraction model is designed by partitioning the environment into multiple belief regions and employed at the high-level navigation planner to estimate the dynamic obstacles' location. This additional location information of dynamic obstacles offered by belief abstraction enables less conservative long-horizon navigation actions beyond guaranteeing immediate collision avoidance. Accordingly, a synthesized action planner sends a set of locomotion actions to the middle-level motion planner while incorporating safe locomotion specifications extracted from safety theorems based on a reduced-order model (ROM) of the locomotion process. The motion planner employs the ROM to design safety criteria and a sampling algorithm to generate nonperiodic motion plans that accurately track high-level actions. At the low level, a foot placement controller based on an angular-momentum linear inverted pendulum model is implemented and integrated with an ankle-actuated passivity-based controller for full-body trajectory tracking. To address external perturbations, this study also investigates the safe sequential composition of the keyframe locomotion state and achieves robust transitions against external perturbations through reachability analysis. The overall TAMP framework is validated with extensive simulations and hardware experiments on bipedal walking robots Cassie and Digit designed by Agility Robotics. 

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5. Integrating social and cognitive aspects of belief dynamics: towards a unifying framework

   https://doi.org/10.1098/rsif.2020.0857  

   Galesic, Mirta; Olsson, Henrik; Dalege, Jonas; van der Does, Tamara; Stein, Daniel L. (March 2021, Journal of The Royal Society Interface)

   null (Ed.)

   Belief change and spread have been studied in many disciplines—from psychology, sociology, economics and philosophy, to biology, computer science and statistical physics—but we still do not have a firm grasp on why some beliefs change more easily and spread faster than others. To fully capture the complex social-cognitive system that gives rise to belief dynamics, we first review insights about structural components and processes of belief dynamics studied within different disciplines. We then outline a unifying quantitative framework that enables theoretical and empirical comparisons of different belief dynamic models. This framework uses a statistical physics formalism, grounded in cognitive and social theory, as well as empirical observations. We show how this framework can be used to integrate extant knowledge and develop a more comprehensive understanding of belief dynamics. 

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