Planning in Discrete and Continuous Markov Decision Processes by Probabilistic Programming

Nitti, Davide Oscar; Belle, Vaishak; Raedt, Luc De

doi:10.1007/978-3-319-23525-7_20

Cited by 13 publications

(9 citation statements)

References 16 publications

(20 reference statements)

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“…Related to these are relational probabilistic models [67,66,32,21]. Although limited accounts for dynamic domains are common here [50,69], explicit actions are seldom addressed in a general way. We refer interested readers to discussions in [15], where differences are also drawn to prior developments in reasoning about actions, including stochastic but non-epistemic GOLOG dialects [37].…”

Section: Related Work and Discussionmentioning

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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Section: Related Work and Discussionmentioning

confidence: 99%

Logic, Probability and Action: A Situation Calculus Perspective

Belle

2020

Lecture Notes in Computer Science

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“…An alternative approach to Problog is the framework of distributional clauses (DC) by Gutmann et al (2011), which extends Problog allowing to define continuous probability distributions over rules and atoms. DC has been used for robotic manipulation and grasping in Moldovan and De Raedt (2014); Nitti et al (2015); Moldovan et al (2018).…”

Section: Probabilistic Logic Programmingmentioning

confidence: 99%

Logic programming for deliberative robotic task planning

2023

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Over the last decade, the use of robots in production and daily life has increased. With increasingly complex tasks and interaction in different environments including humans, robots are required a higher level of autonomy for efficient deliberation. Task planning is a key element of deliberation. It combines elementary operations into a structured plan to satisfy a prescribed goal, given specifications on the robot and the environment. In this manuscript, we present a survey on recent advances in the application of logic programming to the problem of task planning. Logic programming offers several advantages compared to other approaches, including greater expressivity and interpretability which may aid in the development of safe and reliable robots. We analyze different planners and their suitability for specific robotic applications, based on expressivity in domain representation, computational efficiency and software implementation. In this way, we support the robotic designer in choosing the best tool for his application.

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“…The idea of using inference for stochastic planning planning has a long history and has attracted many different approaches. For example, Cooper [1988] showed how inference can be used for decision making in influence diagrams, Domshlak and Hoffmann [2006] use an approach based on weighted model counting, Nitti et al [2015] use a probabilistic programming formulation, and Lee et al [2016] use anytime marginal MAP solvers for planning problems.…”

Section: Related Workmentioning

confidence: 99%

Approximate Inference for Stochastic Planning in Factored Spaces

Wu¹,

Khardon²

2022

Preprint

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The paper explores the use of approximate inference techniques as solution methods for stochastic planning problems with discrete factored spaces. While much prior work exists on this topic, subtle variations hinder a global understanding of different approaches for their differences and potential advantages. Here we abstract a simple framework that captures and connects prior work along two dimensions, direction of information flow, i.e., forward vs. backward inference, and the type of approximation used, e.g., Belief Propagation (BP) vs. mean field variational inference (MFVI). Through this analysis we also propose a novel algorithm, CSVI, which provides a tighter variational approximation compared to prior work. An extensive experimental evaluation compares algorithms from different branches of the framework, showing that methods based on BP are generally better than methods based on MFVI, that CSVI is competitive with BP algorithms, and that while inference direction does not show a significant effect for VI methods, forward inference provides stronger performance with BP.

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Planning in Discrete and Continuous Markov Decision Processes by Probabilistic Programming

Cited by 13 publications

References 16 publications

Logic, Probability and Action: A Situation Calculus Perspective

Logic, Probability and Action: A Situation Calculus Perspective

Logic programming for deliberative robotic task planning

Approximate Inference for Stochastic Planning in Factored Spaces

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