Distribution sur les croyances pour la planification de Dec-POMDP PSMBDP

Corona, Gabriel; Charpillet, François

doi:10.3166/ria.24.525-544

Cited by 1 publication

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“…2 It might be possible, of course, to define a notion of program correctness that works for any observed value, in a style similar to the offline computation of policies [53][54][55]. We gloss over this issue for now.…”

Section: Allegromentioning

confidence: 99%

Logic + probabilistic programming + causal laws

Belle

2023

R. Soc. open sci.

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Probabilistic planning attempts to incorporate stochastic models directly into the planning process, which is the problem of synthesizing a sequence of actions that achieves some objective for a putative agent. Probabilistic programming has rapidly emerged as a key paradigm to integrate probabilistic concepts with programming languages, which allows one to specify complex probabilistic models using programming primitives like recursion and loops. Probabilistic logic programming aims to further ease the specification of structured probability distributions using first-order logical artefacts. In this article, we briefly discuss the modelling of probabilistic planning through the lens of probabilistic (logic) programming. Although many flavours for such an integration are possible, we focus on two representative examples. The first is an extension to the popular probabilistic logic programming language PROBLOG, which permits the decoration of probabilities on Horn clauses—that is, prolog programs. The second is an extension to the popular agent programming language GOLOG, which permits the logical specification of dynamical systems via actions, effects and observations. The probabilistic extensions thereof emphasize different strengths of probabilistic programming that are particularly useful for non-trivial modelling issues raised in probabilistic planning. Among other things, one can instantiate planning problems with growing and shrinking state spaces, discrete and continuous probability distributions, and non-unique prior distributions in a first-order setting.

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Section: Allegromentioning

confidence: 99%

Logic + probabilistic programming + causal laws

Belle

2023

R. Soc. open sci.

View full text Add to dashboard Cite

show abstract

Distribution sur les croyances pour la planification de Dec-POMDP PSMBDP

Cited by 1 publication

References 0 publications

Logic + probabilistic programming + causal laws

Logic + probabilistic programming + causal laws

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