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1. A Logic Programming Approach to Regression Based Repair of Incorrect Initial Belief States

   Loc Pham, Enrico Pontelli ( July 2020 , International Conference on Logic Programming)

   null (Ed.)

   This paper introduces a combination of regression and belief revision to allow agents to deal with inconsistencies while executing plans. 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 and supports arbitrary sequences of actions. The paper illustrates how logic programming can be effectively used to support these processes. 

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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. State Transition in Multi-agent Epistemic Domains Using Answer Set Programming

   Izmirlioglu, Yusuf ; Pham, Loc ; Cao Son, Tran ; Pontelli, Enrico ( August 2022 , Lecture notes in computer science)

   In this paper we develop a state transition function for partially observable multi-agent epistemic domains and implement it using Answer Set Programming (ASP). The transition function computes the next state upon an occurrence of a single action. Thus it can be used as a module in epistemic planners. Our transition function incorporates ontic, sensing and announcement actions and allows for arbitrary nested belief formulae and general common knowledge. A novel feature of our model is that upon an action occurrence, an observing agent corrects his (possibly wrong) initial beliefs about action precondition and his observability. By examples, we show that this step is necessary for robust state transition. We establish some properties of our state transition function regarding its soundness in updating beliefs of agents consistent with their observability. 

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4. The blindfolded traveler’s problem: A search framework for motion planning with contact estimates

   https://doi.org/10.1177/02783649231170893  

   Saund, Brad ; Choudhury, Sanjiban ; Srinivasa, Siddhartha ; Berenson, Dmitry ( May 2023 , The International Journal of Robotics Research)

   We address the problem of robot motion planning under uncertainty where the only observations are through contact with the environment. Such problems are typically solved by planning optimistically assuming unknown space is free, moving along the planned path and re-planning if the robot collides. However this approach can be very inefficient, leading to many unnecessary collisions and unproductive motion. We propose a new formulation, the Blindfolded Traveler’s Problem (BTP), for planning on a graph containing edges with unknown validity, with true validity observed only through attempted traversal by the robot. The solution to a BTP is a policy indicating the next edge to attempt given previous observations and an initial belief. We prove that BTP is NP-complete and show that exact modeling of the belief is intractable, therefore we present several approximation-based policies and beliefs. For the policy we propose graph search with edge weights augmented by the probability of collision. For the belief representation we propose a weighted Mixture of Experts of Collision Hypothesis Sets and a Manifold Particle Filter. Empirical evaluation in simulation and on a real robot arm shows that our proposed approach vastly outperforms several baselines as well as a previous approach that does not employ the BTP framework.

    

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5. A New Semantics for Action Language m∗

   Pham, Loc ; Izmirlioglu, Yusuf ; Cao Son, Tran ; Pontelli, Enrico ( November 2022 , Lecture notes in computer science)

   The action language m∗ employs the notion of update models in defining transitions between states. Given an action occurrence and a state, the update model of the action occurrence is automatically constructed from the given state and the observability of agents. A main criticism of this approach is that it cannot deal with situations when agents’ have incorrect beliefs about the observability of other agents. The present paper addresses this shortcoming by defining a new semantics for m∗ . The new semantics addresses the aforementioned problem of m∗ while maintaining the simplicity of its semantics; the new definitions continue to employ simple update models, with at most three events for all types of actions, which can be constructed given the action specification and independently from the state in which the action occurs. 

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