Knowledge Compilation for Belief Change

Pagnucco, Maurice

doi:10.1007/11941439_13

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…Pagnucco et al [43,53] have focused on the representation of beliefs with prime implicates. They showed at first how an epistemic entrenchment [21] can be encoded in a compact way.…”

Section: Related Workmentioning

confidence: 99%

Prime forms and minimal change in propositional belief bases

Marchi

Bittencourt

Perrussel

2010

Ann Math Artif Intell

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This paper proposes to use prime implicants and prime implicates normal forms to represent belief sets. This representation is used, on the one hand, to define syntactical versions of belief change operators that also satisfy the rationality postulates but present better complexity properties than those proposed in the literature and, on the other hand, to propose a new minimal distance that adopts as a minimal belief unit a "fact", defined as a prime implicate of the belief set, instead of the usually adopted Hamming distance, i.e., the number of propositional symbols on which the models differ. Some experiments are also presented that show that this new minimal distance allows to define belief change operators that usually preserve more information of the original belief set.

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“…Pagnucco et al [43,53] have focused on the representation of beliefs with prime implicates. They showed at first how an epistemic entrenchment [21] can be encoded in a compact way.…”

Section: Related Workmentioning

confidence: 99%

Prime forms and minimal change in propositional belief bases

Marchi

Bittencourt

Perrussel

2010

Ann Math Artif Intell

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“…Recently, new studies of implementation belief revision by Horn Clauses have been initiated (Booth et al, 2010;Delgrande, 2008;Delgrande and Wassermann, 2010). Pagnucco (Pagnucco, 2006) and (Zhuang et al, 2007) formalised a way of implementing AGM operations using a knowledge compilation technique involving prime implicates in order to improve computational efficiency. The study (Schwind et al, 2019) has proposed a change formula, that given two know bases (or the same knowledge base at different times) it is possible to determine what caused the change in the belief set.…”

Section: Artificial Intelligence In Cognitive Rehabilitationmentioning

confidence: 99%

AI-Rehab: A Framework for AI Driven Neurorehabilitation Training - The Profiling Challenge

Almeida

Sirsat

Badia

et al. 2020

Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies

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One of the health clinic challenges is rehabilitation therapy cognitive impairment that can happen after brain injury, dementia and in normal cognitive decline due to aging. Current cognitive rehabilitation therapy has been shown to be the most effective way to address this problem. However, a) it is not adaptive for every patient, b) it has a high cost, and c) it is usually implemented in clinical environments. The Task Generator (TG) is a free tool for the generation of cognitive training tasks. However, TG is not designed to adapt and monitor the cognitive progress of the patient. Hence, we propose in the BRaNT project an enhancement of TG with belief revision and machine learning techniques, gamification and remote monitoring capabilities to enable health professionals to provide a long-term personalized cognitive rehabilitation therapy at home. The BRaNT is an interdisciplinary effort that addresses scientific limitations of current practices as well as provides solutions towards the sustainability of health systems and contributes towards the improvement of quality of life of patients. This paper proposes the AI-Rehab framework for the BRaNT, explains profiling challenge in the situation of insufficient data and presents an alternate AI solutions which might be applicable once enough data is available.

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“…[21,22]), and there are several algorithms for calculating it (e.g., [21,6,14,25,22]; see [4] for more). This concept has been already used for epistemic concerns, mainly on proposals following the AGM approach for belief revision [1] in which the agent's beliefs are represented syntactically (e.g., [19,38]). Here it will be used to simplify the model operation defined of the next section for representing the action of forgetting π.…”

Section: Basic Definitionsmentioning

confidence: 99%

Forgetting complex propositions

et al. 2015

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This paper uses possible-world semantics to model the changes that may occur in an agent's knowledge as she loses information. This builds on previous work in which the agent may forget the truth-value of an atomic proposition, to a more general case where she may forget the truth-value of a propositional formula. The generalization poses some challenges, since in order to forget whether a complex proposition π is the case, the agent must also lose information about the propositional atoms that appear in it, and there is no unambiguous way to go about this.We resolve this situation by considering expressions of the form [ ‡π]ϕ, which quantify over all possible (but 'minimal') ways of forgetting whether π. Propositional atoms are modified non-deterministically, although uniformly, in all possible worlds. We then represent this within action model logic in order to give a sound and complete axiomatization for a logic with knowledge and forgetting. Finally, some variants are discussed, such as when an agent forgets π (rather than forgets whether π) and when the modification of atomic facts is done non-uniformly throughout the model.

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Knowledge Compilation for Belief Change

Cited by 8 publications

References 13 publications

Prime forms and minimal change in propositional belief bases

Prime forms and minimal change in propositional belief bases

AI-Rehab: A Framework for AI Driven Neurorehabilitation Training - The Profiling Challenge

Forgetting complex propositions

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