FluCaP: A Heuristic Search Planner for First-Order MDPs

Hölldobler, Steffen; Karabaev, Eldar; Skvortsova, Olga

doi:10.1613/jair.1965

Cited by 19 publications

(22 citation statements)

References 14 publications

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“…The conjunctive form of states prohibits universal quantification and explicit negation (of either formulas, or single atoms), and partitionings can only be specified non-declaratively using decision lists, but together with constraint atoms the language is powerful enough for many interesting RMDPs. Related approaches such as the ones by Hölldobler et al (2006) and are based on similar, existentially quantified languages.…”

Section: Definition 629mentioning

confidence: 99%

“…A notable exception is the related relational IDP system FLUCAP by Hölldobler et al (2006) in which the creation of new variables is allowed. 40 In fact, it is also possible to have a goal on(X, Y) that merely states that some block should be on something.…”

Section: Overlaps Regression and Weakest Preconditionsmentioning

confidence: 99%

“…Another form of approximation is introduced in the FOVI extension by Karabaev and Skvortsova (2005) (see also Hölldobler et al, 2006;Skvortsova, 2006a, on its implementation FLUCAP) in which it is extended to first order LAO* (FOLAO*) with a heuristic search that avoids evaluating all states. FOLAO* is a direct first-order upgrade of the propositional LAO* algorithm (Feng and Hansen, 2002).…”

Section: Dynamic Representationsmentioning

confidence: 99%

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Logic of Adaptive Behavior

Otterlo¹

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PrefaceOne the central themes of this book is the interplay between representation and behavior. Representation is about the way we look at our world, about how we see the things around us, and about how we think about and evaluate the concepts and knowledge that shape our surroundings.Behavior is about what we do, about our actions in the world or the mental actions in our head, and about the consequences of a wide variety of activities we engage in every day. Our representation of the world determines our behavior, but at the same time, our behavior influences what we see, and with that, it changes our representation of the world. This strange, mutually modifying loop is a fascinating phenomenon and it is the core object of study in both psychology and artificial intelligence. Indeed, much of the material in this book is about formal models and algorithms that characterize how computer-based, intelligent systems can represent their mathematical worlds and how they can behave rationally in such worlds. However, the pattern of this loop is much more general, and can be observed almost anywhere. For example, every paper I read changes my representation of my own scientific world, but at the same time, my research activities determine my choices for further study.Out of the many possible things that may be present in your representation of your world, and that may be part of your behavioral pattern, are people. Most of our representations of our world, our thinking, our talking, and our behavioral patterns, involve people. Especially in the path leading to this book, my representation of the world, and my course of action, have been influenced by so many people. For starters, in the writing of this book I have been influenced by many great scientists. There are many of them who I know personally and with whom I had the privilege to engage in interesting interactions, but there are many others that have influenced me without them knowing, for example when I attended their talks or read their books and papers. At this place, I want to express my gratitude to a number of specific people that have influenced me, helped me, supported me and made my life so much more interesting and enjoyable.First of all, I would like to thank my promotor Anton Nijholt for giving me the opportunity to do a PhD in his research group, and for the complete freedom and support he has given me to pursue my own interests, even when my path led me to new topics, to Utrecht and even to Freiburg. I thank Mannes Poel for all his support, his open-door policy that made it possible to just drop by with a coffee and start a discussion on any topic that was on my mind at those specific moments, for all the fun lunch walks, the stimulating educational activities we did together and all the other things that made our interactions fun and interesting.Many thanks go to my promotor John-Jules Meyer. His endless kindness, support and expertise have had great influence on my journey through my PhD period. I vividly remember our productive discussions, alwa...

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Section: Definition 629mentioning

confidence: 99%

Section: Overlaps Regression and Weakest Preconditionsmentioning

confidence: 99%

Section: Dynamic Representationsmentioning

confidence: 99%

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Logic of Adaptive Behavior

Otterlo¹

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“…Only a simple boxworld example with human-assisted computation is demonstrated in the SDP work, but the method serves as a basis for FOALP (Sanner & Boutilier, 2009), which replaces exact techniques with heuristic approximation in order to scale the techniques to benchmark planning domains. Application of FOVI on planning domains is only demonstrated on the colored blocksworld benchmark, and is limited to under 10 blocks (Holldobler, Karabaev, & Skvortsova, 2006).…”

Section: A24 Exact Value Iteration In First-order Mdpsmentioning

confidence: 99%

Automatic Induction of Bellman-Error Features for Probabilistic Planning

Wu¹,

Givan²

2010

jair

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Domain-specific features are important in representing problem structure throughout machine learning and decision-theoretic planning. In planning, once state features are provided, domainindependent algorithms such as approximate value iteration can learn weighted combinations of those features that often perform well as heuristic estimates of state value (e.g., distance to the goal). Successful applications in real-world domains often require features crafted by human experts. Here, we propose automatic processes for learning useful domain-specific feature sets with little or no human intervention. Our methods select and add features that describe state-space regions of high inconsistency in the Bellman equation (statewise Bellman error) during approximate value iteration. Our method can be applied using any real-valued-feature hypothesis space and corresponding learning method for selecting features from training sets of state-value pairs. We evaluate the method with hypothesis spaces defined by both relational and propositional feature languages, using nine probabilistic planning domains. We show that approximate value iteration using a relational feature space performs at the state-of-the-art in domain-independent stochastic relational planning. Our method provides the first domain-independent approach that plays Tetris successfully (without human-engineered features).

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“…Taking advantage of such relational structure, [2] developed the SDP algorithm that was later elaborated in several formalisms and systems [9], [5], [13], [17]. One of the important ideas in SDP was to represent stochastic actions as deterministic alternatives under nature's control.…”

Section: Introductionmentioning

confidence: 99%

Abstract planning for reactive robots

Joshi

Schermerhorn

Khardon

et al. 2012

2012 IEEE International Conference on Robotics and Automation

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Abstract-Hybrid reactive-deliberative architectures in robotics combine reactive sub-policies for fast action execution with goal sequencing and deliberation. The need for replanning, however, presents a challenge for reactivity and hinders the potential for guarantees about the plan quality. In this paper, we argue that one can integrate abstract planning provided by symbolic dynamic programming in first order logic into a reactive robotic architecture, and that such an integration is in fact natural and has advantages over traditional approaches. In particular, it allows the integrated system to spend off-line time planning for a policy, and then use the policy reactively in open worlds, in situations with unexpected outcomes, and even in new environments, all by simply reacting to a state change executing a new action proposed by the policy. We demonstrate the viability of the approach by integrating the FODD-Planner with the robotic DIARC architecture showing how an appropriate interface can be defined and that this integration can yield robust goal-based action execution on robots in open worlds.

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FluCaP: A Heuristic Search Planner for First-Order MDPs

Cited by 19 publications

References 14 publications

Logic of Adaptive Behavior

Logic of Adaptive Behavior

Automatic Induction of Bellman-Error Features for Probabilistic Planning

Abstract planning for reactive robots

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