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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. 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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5. Belief-prefix Control for Autonomously Dodging Switching Disturbances

   https://doi.org/10.23919/ECC51009.2020.9143990  

   Rutledge, Kwesi; Ozay, Necmiye (January 2020, European Control Conference)

   null (Ed.)

   This paper presents a new method of controller synthesis for hidden mode switched systems, where the disturbances are the quantities that are affected by the unobserved switches. Rather than using model discrimination techniques that rely on modifying desired control actions to achieve identification, the controller uses consistency sets which map the measured external behaviors to a belief about which mode signal is being executed and a control action. This hybrid controller is a prefix-based controller, where the prefixes come from an offline constructed belief graph that incorporates prior information about switching sequences with potential reachable sets of the dynamics. While the mode signal is hidden to the controller, the system’s location on the belief graph is fully observed and allows for this problem to be transformed into a design problem in which a discrete mode, in terms of beliefs, is directly observed. Finally, it is shown that affine controllers dependent on prefixes of such beliefs can be synthesized via linear programming. 

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