Results 1  10
of
92
Recent Advances in AI Planning
 AI MAGAZINE
, 1999
"... The past five years have seen dramatic advances in planning algorithms, with an emphasis on propositional methods such as Graphplan and compilers that convert planning problems into propositional CNF formulae for solution via systematic or stochastic SAT methods. Related work on the Deep Space O ..."
Abstract

Cited by 123 (0 self)
 Add to MetaCart
The past five years have seen dramatic advances in planning algorithms, with an emphasis on propositional methods such as Graphplan and compilers that convert planning problems into propositional CNF formulae for solution via systematic or stochastic SAT methods. Related work on the Deep Space One spacecraft control algorithms advances our understanding of interleaved planning and execution. In this survey,we explain the latest techniques and suggest areas for future research.
Planning under continuous time and resource uncertainty: A challenge for AI
 In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence
, 2002
"... yQSS Group Inc. zQSS Group Inc. xRIACS experiment is assigned a scientific value). Different observations and experiments take differing amounts of time and consume differing amounts of power and data storage.There are, in general, a number of constraints that govern the rovers activities: ffl Ther ..."
Abstract

Cited by 118 (19 self)
 Add to MetaCart
(Show Context)
yQSS Group Inc. zQSS Group Inc. xRIACS experiment is assigned a scientific value). Different observations and experiments take differing amounts of time and consume differing amounts of power and data storage.There are, in general, a number of constraints that govern the rovers activities: ffl There are time, power, data storage, and positioning constraints for performing different activities. Time constraints often result from illuminationrequirementthat is, experiments may require that a target rock or sample be illuminated with a certain intensity, or from a certain angle.
Bridging the gap between planning and scheduling
 KNOWLEDGE ENGINEERING REVIEW
, 2000
"... Planning research in Artificial Intelligence (AI) has often focused on problems where there are cascading levels of action choice and complex interactions between actions. In contrast, Scheduling research has focused on much larger problems where there is little action choice, but the resulting orde ..."
Abstract

Cited by 113 (11 self)
 Add to MetaCart
(Show Context)
Planning research in Artificial Intelligence (AI) has often focused on problems where there are cascading levels of action choice and complex interactions between actions. In contrast, Scheduling research has focused on much larger problems where there is little action choice, but the resulting ordering problem is hard. In this paper, we give an overview of AI planning and scheduling techniques, focusing on their similarities, differences, and limitations. We also argue that many difficult practical problems lie somewhere between planning and scheduling, and that neither area has the right set of tools for solving these vexing problems.
Exploiting Structure to Efficiently Solve Large Scale Partially Observable Markov Decision Processes
, 2005
"... Partially observable Markov decision processes (POMDPs) provide a natural and principled framework to model a wide range of sequential decision making problems under uncertainty. To date, the use of POMDPs in realworld problems has been limited by the poor scalability of existing solution algorithm ..."
Abstract

Cited by 89 (6 self)
 Add to MetaCart
Partially observable Markov decision processes (POMDPs) provide a natural and principled framework to model a wide range of sequential decision making problems under uncertainty. To date, the use of POMDPs in realworld problems has been limited by the poor scalability of existing solution algorithms, which can only solve problems with up to ten thousand states. In fact, the complexity of finding an optimal policy for a finitehorizon discrete POMDP is PSPACEcomplete. In practice, two important sources of intractability plague most solution algorithms: large policy spaces and large state spaces. On the other hand,
Learning symbolic models of stochastic domains
 Journal of Artificial Intelligence Research
"... In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such rules can be effectively learned. Through experi ..."
Abstract

Cited by 82 (3 self)
 Add to MetaCart
(Show Context)
In this article, we work towards the goal of developing agents that can learn to act in complex worlds. We develop a probabilistic, relational planning rule representation that compactly models noisy, nondeterministic action effects, and show how such rules can be effectively learned. Through experiments in simple planning domains and a 3D simulated blocks world with realistic physics, we demonstrate that this learning algorithm allows agents to effectively model world dynamics. 1.
Contingent Planning Under Uncertainty via Stochastic Satisfiability
 Artificial Intelligence
, 1999
"... We describe two new probabilistic planning techniques cmaxplan and zanderthat generate contingent plans in probabilistic propositional domains. Both operate by transforming the planning problem into a stochastic satisfiability problem and solving that problem instead. cmaxplan encodes t ..."
Abstract

Cited by 66 (11 self)
 Add to MetaCart
(Show Context)
We describe two new probabilistic planning techniques cmaxplan and zanderthat generate contingent plans in probabilistic propositional domains. Both operate by transforming the planning problem into a stochastic satisfiability problem and solving that problem instead. cmaxplan encodes the problem as an EMajsat instance, while zander encodes the problem as an SSat instance. Although SSat problems are in a higher complexity class than EMajsat problems, the problem encodings produced by zander are substantially more compact and appear to be easier to solve than the corresponding EMajsat encodings. Preliminary results for zander indicate that it is competitive with existing planners on a variety of problems. Introduction When planning under uncertainty, any information about the state of the world is precious. A contingent plan is one that can make action choices contingent on such information. In this paper, we present an implemented framework for contingent pl...
Inductive policy selection for firstorder MDPs
 In UAI
, 2002
"... We select policies for large Markov Decision Processes (MDPs) with compact firstorder representations. We find policies that generalize well as the number of objects in the domain grows, potentially without bound. Existing dynamicprogramming approaches based on flat, propositional, or firstorder ..."
Abstract

Cited by 48 (15 self)
 Add to MetaCart
(Show Context)
We select policies for large Markov Decision Processes (MDPs) with compact firstorder representations. We find policies that generalize well as the number of objects in the domain grows, potentially without bound. Existing dynamicprogramming approaches based on flat, propositional, or firstorder representations either are impractical here or do not naturally scale as the number of objects grows without bound. We implement and evaluate an alternative approach that induces firstorder policies using training data constructed by solving small problem instances using PGraphplan (Blum & Langford, 1999). Our policies are represented as ensembles of decision lists, using a taxonomic concept language. This approach extends the work of Martin and Geffner (2000) to stochastic domains, ensemble learning, and a wider variety of problems. Empirically, we find “good ” policies for several stochastic firstorder MDPs that are beyond the scope of previous approaches. We also discuss the application of this work to the relational reinforcementlearning problem. 1
Exploiting FirstOrder Regression in Inductive Policy Selection
 Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI’04
, 2004
"... We consider the problem of computing optimal generalised policies for relational Markov decision processes. We describe an approach combining some of the benefits of purely inductive techniques with those of symbolic dynamic programming methods. The latter reason about the optimal value function usi ..."
Abstract

Cited by 47 (2 self)
 Add to MetaCart
(Show Context)
We consider the problem of computing optimal generalised policies for relational Markov decision processes. We describe an approach combining some of the benefits of purely inductive techniques with those of symbolic dynamic programming methods. The latter reason about the optimal value function using firstorder decisiontheoretic regression and formula rewriting, while the former, when provided with a suitable hypotheses language, are capable of generalising value functions or policies for small instances. Our idea is to use reasoning and in particular classical firstorder regression to automatically generate a hypotheses language dedicated to the domain at hand, which is then used as input by an inductive solver. This approach avoids the more complex reasoning of symbolic dynamic programming while focusing the inductive solver’s attention on concepts that are specifically relevant to the optimal value function for the domain considered. 1
Learning probabilistic relational planning rules
, 2003
"... To learn to behave in highly complex domains, agents must represent and learn compact models of the world dynamics. In this paper, we present an algorithm for learning probabilistic STRIPSlike planning operators from examples. We demonstrate the effective learning of rulebased operators for a wide ..."
Abstract

Cited by 44 (5 self)
 Add to MetaCart
(Show Context)
To learn to behave in highly complex domains, agents must represent and learn compact models of the world dynamics. In this paper, we present an algorithm for learning probabilistic STRIPSlike planning operators from examples. We demonstrate the effective learning of rulebased operators for a wide range of traditional planning domains.