Belief Revision and Progression of Knowledge Bases in the Epistemic Situation Calculus

Authors: Christoph Schwering, Gerhard Lakemeyer, Maurice Pagnucco

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Reproducibility Variable Result LLM Response
Research Type Theoretical In this paper we propose a novel framework for computing progression in the epistemic situation calculus. In particular, we model an agent’s preferential belief structure using conditional statements and provide a technique for updating these conditional statements as actions are performed and sensing information is received. Moreover, we show, by using the concepts of natural revision and only-believing, that the progression of a conditional knowledge base can be represented by only-believing the revised set of conditional statements. These results lay the foundations for feasible belief progression due to the unique-model property of only-believing.
Researcher Affiliation Academia Christoph Schwering RWTH Aachen University Aachen, Germany schwering@kbsg.rwth-aachen.de Gerhard Lakemeyer RWTH Aachen University Aachen, Germany gerhard@kbsg.rwth-aachen.de Maurice Pagnucco University of New South Wales Sydney, Australia morri@cse.unsw.edu.au
Pseudocode No The paper provides formal definitions and theorems but does not include any pseudocode or clearly labeled algorithm blocks.
Open Source Code No The paper does not provide any concrete access to source code, such as a repository link or an explicit statement of code release for the methodology described.
Open Datasets No The paper does not describe experiments using publicly available datasets, and therefore no concrete access information for a dataset is provided.
Dataset Splits No The paper does not describe any experiments that would involve dataset splits.
Hardware Specification No The paper does not describe any experiments that would require hardware specifications.
Software Dependencies No The paper does not provide specific ancillary software details with version numbers, as it focuses on theoretical contributions rather than implementation.
Experiment Setup No The paper does not describe any experimental setup details such as hyperparameter values or training configurations.