A correctness result for synthesizing plans with loops in stochastic domains

Treszkai, Laszlo; Belle, Vaishak

doi:10.1016/j.ijar.2019.12.005

Cited by 6 publications

(2 citation statements)

References 22 publications

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“…But of course, such a policy could also be synthesized. There is considerable work on the induction of recursive program-like structures [ 67 ]. Thus, the situation calculus provides a comprehensive framework to reason about different sorts of policies and verify their properties.…”

Section: A Qualitative Comparisonmentioning

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: A Qualitative Comparisonmentioning

confidence: 99%

Logic + probabilistic programming + causal laws

Belle

2023

R. Soc. open sci.

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“…How the correctness of such plans should be generalized to noisy environments was considered in [8,3]. The algorithmic synthesis problem was then considered in [81].…”

Section: High-level Controlmentioning

confidence: 99%

Logic, Probability and Action: A Situation Calculus Perspective

Belle

2020

Lecture Notes in Computer Science

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The unification of logic and probability is a long-standing concern in AI, and more generally, in the philosophy of science. In essence, logic provides an easy way to specify properties that must hold in every possible world, and probability allows us to further quantify the weight and ratio of the worlds that must satisfy a property. To that end, numerous developments have been undertaken, culminating in proposals such as probabilistic relational models. While this progress has been notable, a general-purpose first-order knowledge representation language to reason about probabilities and dynamics, including in continuous settings, is still to emerge. In this paper, we survey recent results pertaining to the integration of logic, probability and actions in the situation calculus, which is arguably one of the oldest and most well-known formalisms. We then explore reduction theorems and programming interfaces for the language. These results are motivated in the context of cognitive robotics (as envisioned by Reiter and his colleagues) for the sake of concreteness. Overall, the advantage of proving results for such a general language is that it becomes possible to adapt them to any special-purpose fragment, including but not limited to popular probabilistic relational models.

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Analyzing generalized planning under nondeterminism

Belle

2022

Artificial Intelligence

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A correctness result for synthesizing plans with loops in stochastic domains

Cited by 6 publications

References 22 publications

Logic + probabilistic programming + causal laws

Logic + probabilistic programming + causal laws

Logic, Probability and Action: A Situation Calculus Perspective

Analyzing generalized planning under nondeterminism

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