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<title>Journal of Artificial Intelligence Research</title>
<description>JAIR (ISSN 11076 - 9757) covers all areas of artificial intelligence (AI), publishing refereed research articles, survey articles, and technical notes.</description>
<link>http://jair.org</link>
<copyright>&#x00a9; Copyright 1993-2008 AI Access Foundation, Inc.</copyright>
<ttl>1440</ttl>
      <pubDate>Mon, 30 Aug 2010 20:45:41 -0500</pubDate>
<lastBuildDate>30 Aug 2010 20:38:55 UT</lastBuildDate>

<item>
  <title>Using Local Alignments for Relation Recognition</title>
  <link>http://www.jair.org/papers/paper2964.html</link>
  <description><![CDATA[This paper discusses the problem of marrying structural similarity with semantic relatedness for Information Extraction from text. Aiming at accurate recognition of relations, we introduce local alignment kernels and explore various possibilities of using them for this task. We give a definition of a local alignment (LA) kernel based on the Smith-Waterman score as a sequence similarity measure and proceed with a range of possibilities for computing similarity between elements of sequences. We show how distributional similarity measures obtained from unlabeled data can be incorporated into the learning task as semantic knowledge. Our experiments suggest that the LA kernel yields promising results on various biomedical corpora outperforming two baselines by a large margin. Additional series of experiments have been conducted on the data sets of seven general relation types, where the performance of the LA kernel is comparable to the current state-of-the-art results.]]></description>
  <author>S.  Katrenko, P.  W. Adriaans and M.  van Someren</author>
  <category>ML</category>
  <comments>http://www.jair.org/comments.html</comments>
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  <pubDate>Mon, 17 May 2010 18:12:24 -0500</pubDate>
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<item>
  <title>Change in Abstract Argumentation Frameworks: Adding an Argument</title>
  <link>http://www.jair.org/papers/paper2965.html</link>
  <description><![CDATA[In this paper, we address the problem of change in an abstract argumentation system. We focus on a particular change: the addition of  a new argument which interacts with previous arguments. We study the impact of such an addition on the outcome of the argumentation system, more particularly on the set of its extensions. Several properties for this change operation are defined by comparing the new set of extensions to the initial one, these properties are called structural when the comparisons are based on set-cardinality or set-inclusion relations. Several other properties are proposed where comparisons are based on the status of some particular arguments: the accepted arguments; these properties refer to the evolution of this status during the change, e.g., Monotony and Priority to Recency. All these  properties may be more or less desirable according to specific applications. They are studied under two particular semantics: the grounded and preferred semantics.]]></description>
  <author>C.  Cayrol, F.  Dupin de Saint-Cyr and M.  Lagasquie-Schiex</author>
  <category>KR</category>
  <comments>http://www.jair.org/comments.html</comments>
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  <pubDate>Fri, 21 May 2010 10:39:05 -0500</pubDate>
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<item>
  <title>BnB-ADOPT: An Asynchronous Branch-and-Bound DCOP Algorithm</title>
  <link>http://www.jair.org/papers/paper2849.html</link>
  <description><![CDATA[Distributed constraint optimization (DCOP) problems are a popular way of formulating and solving agent-coordination problems. A DCOP problem is a problem where several agents coordinate their values such that the sum of the resulting constraint costs is minimal. It is often desirable to solve DCOP problems with memory-bounded and asynchronous algorithms. We introduce Branch-and-Bound ADOPT (BnB-ADOPT), a memory-bounded asynchronous DCOP search algorithm that uses the message-passing and communication framework of ADOPT (Modi, Shen, Tambe, &amp; Yokoo, 2005), a well known memory-bounded asynchronous DCOP search algorithm, but changes the search strategy of ADOPT from best-first search to depth-first branch-and-bound search. Our experimental results show that BnB-ADOPT finds cost-minimal solutions up to one order of magnitude faster than ADOPT for a variety of large DCOP problems and is as fast as NCBB, a memory-bounded synchronous DCOP search algorithm, for most of these DCOP problems. Additionally, it is often desirable to find bounded-error solutions for DCOP problems within a reasonable amount of time since finding cost-minimal solutions is NP-hard. The existing bounded-error approximation mechanism allows users only to specify an absolute error bound on the solution cost but a relative error bound is often more intuitive. Thus, we present two new bounded-error approximation mechanisms that allow for relative error bounds and implement them on top of BnB-ADOPT.]]></description>
  <author>W.  Yeoh, A.  Felner and S.  Koenig</author>
  <category>Search/CSP</category>
  <comments>http://www.jair.org/comments.html</comments>
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  <pubDate>Sun, 23 May 2010 18:15:24 -0500</pubDate>
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</item>

<item>
  <title>A Survey of Paraphrasing and Textual Entailment Methods</title>
  <link>http://www.jair.org/papers/paper2985.html</link>
  <description><![CDATA[Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.]]></description>
  <author>I.  Androutsopoulos and P.  Malakasiotis</author>
  <category>NLP</category>
  <comments>http://www.jair.org/comments.html</comments>
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  <pubDate>Fri, 28 May 2010 14:54:23 -0500</pubDate>
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<item>
  <title>Constructing Reference Sets from Unstructured, Ungrammatical Text</title>
  <link>http://www.jair.org/papers/paper2937.html</link>
  <description><![CDATA[Vast amounts of text on the Web are unstructured and ungrammatical, such as classified ads, auction listings, forum postings, etc. We call such text “posts.” Despite their inconsistent structure and lack of grammar, posts are full of useful information. This paper presents work on semi-automatically building tables of relational information, called “reference sets,” by analyzing such posts directly. Reference sets can be applied to a number of tasks such as ontology maintenance and information extraction. Our reference-set construction method starts with just a small amount of background knowledge, and constructs tuples representing the entities in the posts to form a reference set. We also describe an extension to this approach for the special case where even this small amount of background knowledge is impossible to discover and use. To evaluate the utility of the machine-constructed reference sets, we compare them to manually constructed reference sets in the context of reference-set-based information extraction. Our results show the reference sets constructed by our method outperform manually constructed reference sets. We also compare the reference-set-based extraction approach using the machine-constructed reference set to supervised extraction approaches using generic features. These results demonstrate that using machine-constructed reference sets outperforms the supervised methods, even though the supervised methods require training data.]]></description>
  <author>M.  Michelson and C.  A. Knoblock</author>
  <category>WebAgent</category>
  <comments>http://www.jair.org/comments.html</comments>
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  <pubDate>Fri, 28 May 2010 23:36:47 -0500</pubDate>
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<item>
  <title>Grounding FO and FO(ID) with Bounds</title>
  <link>http://www.jair.org/papers/paper2980.html</link>
  <description><![CDATA[Grounding is the task of reducing a first-order theory and finite domain to an equivalent propositional theory. It is used as preprocessing phase in many logic-based reasoning systems. Such systems provide a rich first-order input language to a user and can rely on efficient propositional solvers to perform the actual reasoning. 
<br /><br />
Besides a first-order theory and finite domain, the input for grounders contains in many applications also additional data. By exploiting this data, the size of the grounder's output can often be reduced significantly. A common practice to improve the efficiency of a grounder in this context is by manually adding semantically redundant information to the input theory, indicating where and when the grounder should exploit the data. In this paper we present a method to compute and add such redundant information automatically. Our method therefore simplifies the task of writing input theories that can be grounded efficiently by current systems.
	
We first present our method for classical first-order logic (FO) theories. Then we extend it to FO(ID), the extension of FO with inductive definitions, which allows for more concise and comprehensive input theories. We discuss implementation issues and experimentally validate the practical applicability of our method.]]></description>
  <author>J.  Wittocx, M.  Mari&amp;#235;n and M.  Denecker</author>
  <category>KR</category>
  <comments>http://www.jair.org/comments.html</comments>
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  <pubDate>Sat, 29 May 2010 18:58:57 -0500</pubDate>
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</item>

<item>
  <title>Developing Approaches  for Solving a Telecommunications Feature Subscription Problem</title>
  <link>http://www.jair.org/papers/paper2992.html</link>
  <description><![CDATA[Call control features (e.g., call-divert, voice-mail) are primitive options to which users can subscribe off-line to personalise their  service. The configuration of a feature subscription involves choosing and sequencing features from a catalogue and is subject to  constraints that prevent undesirable feature interactions at run-time. When the subscription requested by a user is inconsistent, one  problem is to find an optimal relaxation,  which is a generalisation of the feedback vertex  set problem on directed graphs, and thus it is an NP-hard task. We present several constraint programming formulations of the problem. We also present formulations using partial  weighted maximum Boolean satisfiability and mixed integer linear programming.  We study all these formulations by experimentally comparing them  on a variety of randomly generated instances of the feature subscription problem.]]></description>
  <author>D.  Lesaint, D.  Mehta, B.  O'Sullivan, L.  Quesada and N.  Wilson</author>
  <category>Search/CSP</category>
  <comments>http://www.jair.org/comments.html</comments>
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  <pubDate>Sat, 19 Jun 2010 21:20:28 -0500</pubDate>
  <source url='http://www.jair.org/articles.rss'>Journal of Artificial Intelligence Research</source>
</item>

<item>
  <title>Fast Set Bounds Propagation Using a BDD-SAT Hybrid</title>
  <link>http://www.jair.org/papers/paper3014.html</link>
  <description><![CDATA[Binary Decision Diagram (BDD) based set bounds propagation is a powerful approach to solving set-constraint satisfaction problems. However, prior BDD based techniques in- cur the significant overhead of constructing and manipulating graphs during search. We present a set-constraint solver which combines BDD-based set-bounds propagators with the learning abilities of a modern SAT solver. Together with a number of improvements beyond the basic algorithm, this solver is highly competitive with existing propagation based set constraint solvers.]]></description>
  <author>G.  Gange, P.  J. Stuckey and V.  Lagoon</author>
  <category>Search/CSP</category>
  <comments>http://www.jair.org/comments.html</comments>
  <enclosure url='http://www.jair.org/media/3014/live-3014-5040-jair.pdf' length='591157' type='application/pdf' />
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  <pubDate>Fri, 25 Jun 2010 19:32:47 -0500</pubDate>
  <source url='http://www.jair.org/articles.rss'>Journal of Artificial Intelligence Research</source>
</item>

<item>
  <title>Mixed Strategies in Combinatorial Agency</title>
  <link>http://www.jair.org/papers/paper2961.html</link>
  <description><![CDATA[In many multiagent domains a set of agents exert effort towards a joint outcome, yet the individual effort levels cannot be easily observed. A typical example for such a scenario is routing in communication networks, where the sender can only observe whether the packet reached its destination, but often has no information about the actions of the intermediate routers, which influences the final outcome.  We study a setting where a principal needs to motivate a team of agents whose combination of hidden efforts stochastically determines an outcome.  In a companion paper we devise and study a  basic ''combinatorial agency'' model for this setting, where the principal is restricted to inducing a pure Nash equilibrium.  Here we study various implications of this restriction. First, we show that, in contrast to the case of observable efforts, inducing a mixed-strategies equilibrium may be beneficial for the principal. Second, we present a sufficient condition for technologies for which no gain can be generated. Third, we bound the principal's gain for various families of technologies. Finally, we study the robustness of mixed equilibria to coalitional deviations and the computational hardness of the optimal mixed equilibria.]]></description>
  <author>M.  Babaioff, M.  Feldman and N.  Nisan</author>
  <category>MAS/Cecon</category>
  <comments>http://www.jair.org/comments.html</comments>
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  <pubDate>Tue, 27 Jul 2010 19:00:22 -0500</pubDate>
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</item>

<item>
  <title>Approximate Model-Based Diagnosis Using Greedy Stochastic Search</title>
  <link>http://www.jair.org/papers/paper3025.html</link>
  <description><![CDATA[We propose a StochAstic Fault diagnosis AlgoRIthm, called SAFARI, which trades off guarantees of computing minimal diagnoses for computational efficiency. We empirically demonstrate, using the 74XXX and ISCAS-85 suites of benchmark combinatorial circuits, that SAFARI achieves several orders-of-magnitude speedup over two well-known deterministic algorithms, CDA* and HA*, for multiple-fault diagnoses; further, SAFARI can compute a range of multiple-fault diagnoses that CDA* and HA* cannot. We also prove that SAFARI is optimal for a range of propositional fault models, such as the widely-used weak-fault models (models with ignorance of abnormal behavior). We discuss the optimality of SAFARI in a class of strong-fault circuit models with stuck-at failure modes. By modeling the algorithm itself as a Markov chain, we provide exact bounds on the minimality of the diagnosis computed. SAFARI also displays strong anytime behavior, and will return a diagnosis after any non-trivial inference time.]]></description>
  <author>A.  Feldman, G.  Provan and A.  van Gemund</author>
  <category>Search/CSP</category>
  <comments>http://www.jair.org/comments.html</comments>
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  <pubDate>Tue, 27 Jul 2010 19:10:42 -0500</pubDate>
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<item>
  <title>Resource-Driven Mission-Phasing Techniques for Constrained Agents in Stochastic Environments</title>
  <link>http://www.jair.org/papers/paper3004.html</link>
  <description><![CDATA[Because an agent's resources dictate what actions it can possibly take, it should plan which resources it holds over time carefully, considering its inherent limitations (such as power or payload restrictions), the competing needs of other agents for the same resources, and the stochastic nature of the environment. Such agents can, in general, achieve more of their objectives if they can use --- and even create --- opportunities to change which resources they hold at various times.  Driven by resource constraints, the agents could break their overall missions into an optimal series of phases, optimally reconfiguring their resources at each phase, and optimally using their assigned resources in each phase, given their knowledge of the stochastic environment.
<br /><br />
In this paper, we formally define and analyze this constrained, sequential optimization problem in both the single-agent and multi-agent contexts. We present a family of mixed integer linear programming (MILP) formulations of this problem that can optimally create phases (when phases are not predefined) accounting for costs and limitations in phase creation.  Because our formulations multaneously also find the optimal allocations of resources at each phase and the optimal policies for using the allocated resources at each phase, they exploit structure across these coupled problems. This allows them to find solutions significantly faster(orders of magnitude faster in larger problems) than alternative solution techniques, as we demonstrate empirically.]]></description>
  <author>J.  Wu and E.  H. Durfee</author>
  <category>MAS/Cecon</category>
  <comments>http://www.jair.org/comments.html</comments>
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  <pubDate>Fri, 30 Jul 2010 11:39:36 -0500</pubDate>
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</item>

<item>
  <title>A Minimum Relative Entropy Principle for Learning and Acting</title>
  <link>http://www.jair.org/papers/paper3062.html</link>
  <description><![CDATA[This paper proposes a method to construct an adaptive agent that is universal with respect to a given class of experts, where each expert is designed specifically for a particular environment. This adaptive control problem is formalized as the problem of minimizing the relative entropy of the adaptive agent from the expert that is most suitable for the unknown environment. If the agent is a passive observer, then the optimal solution is the well-known Bayesian predictor. However, if the agent is active, then its past actions need to be treated as causal interventions on the I/O stream rather than normal probability conditions. Here it is shown that the solution to this new variational problem is given by a stochastic controller called the Bayesian control rule, which implements adaptive behavior as a mixture of experts. Furthermore, it is shown that under mild assumptions, the Bayesian control rule converges to the control law of the most suitable expert.]]></description>
  <author>P.  A. Ortega and D.  A. Braun</author>
  <category>Prob</category>
  <comments>http://www.jair.org/comments.html</comments>
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  <pubDate>Mon, 16 Aug 2010 17:36:15 -0500</pubDate>
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<item>
  <title>Algorithms for Closed Under Rational Behavior (CURB) Sets</title>
  <link>http://www.jair.org/papers/paper3070.html</link>
  <description><![CDATA[We provide a series of algorithms demonstrating that solutions according to the fundamental game-theoretic solution concept of closed under rational behavior (CURB) sets in two-player, normal-form games can be computed in polynomial time (we also discuss extensions to n-player games). First, we describe an algorithm that identifies all of a player’s best responses conditioned on the belief that the other player will play from within a given subset of its strategy space. This algorithm serves as a subroutine in a series of polynomial-time algorithms for finding all minimal CURB sets, one minimal CURB set, and the smallest minimal CURB set in a game. We then show that the complexity of finding a Nash equilibrium can be exponential only in the size of a game’s smallest CURB set. Related to this, we show that the smallest CURB set can be an arbitrarily small portion of the game, but it can also be arbitrarily larger than the supports of its only enclosed Nash equilibrium. We test our algorithms empirically and find that most commonly studied academic games tend to have either very large or very small minimal CURB sets.]]></description>
  <author>M.  Benisch, G.  B. Davis and T.  Sandholm</author>
  <category>MAS/Cecon</category>
  <comments>http://www.jair.org/comments.html</comments>
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  <pubDate>Fri, 20 Aug 2010 19:42:08 -0500</pubDate>
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<item>
  <title>Logical Foundations of RDF(S) with Datatypes </title>
  <link>http://www.jair.org/papers/paper3088.html</link>
  <description><![CDATA[The Resource Description Framework (RDF) is a Semantic Web standard that provides a data language, simply called RDF, as well as a lightweight ontology language, called RDF Schema. We investigate embeddings of RDF in logic and show how standard logic programming and description logic technology can be used for reasoning with RDF. We subsequently consider extensions of RDF with datatype support, considering D entailment, defined in the RDF semantics specification, and D* entailment, a semantic weakening of D entailment, introduced by ter Horst. We use the embeddings and properties of the logics to establish novel upper bounds for the complexity of deciding entailment. We subsequently establish two novel lower bounds, establishing that RDFS entailment is PTime-complete and that simple-D entailment is coNP-hard, when considering arbitrary datatypes, both in the size of the entailing graph. The results indicate that RDFS may not be as lightweight as one may expect.]]></description>
  <author>J.  de Bruijn and S.  Heymans</author>
  <category>KR</category>
  <comments>http://www.jair.org/comments.html</comments>
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  <pubDate>Fri, 20 Aug 2010 19:49:58 -0500</pubDate>
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<item>
  <title>Cause Identification from Aviation Safety Incident Reports via Weakly Supervised Semantic Lexicon Construction</title>
  <link>http://www.jair.org/papers/paper2986.html</link>
  <description><![CDATA[The Aviation Safety Reporting System collects voluntarily submitted reports on aviation safety incidents to facilitate research work aiming to reduce such incidents. To effectively reduce these incidents, it is vital to accurately identify why these incidents occurred. More precisely, given a set of possible causes, or shaping factors, this task of cause identification involves identifying all and only those shaping factors that are responsible for the incidents described in a report. We investigate two approaches to cause identification. Both approaches exploit information provided by a semantic lexicon, which is automatically constructed via Thelen and Riloff's Basilisk framework augmented with our linguistic and algorithmic modifications. The first approach labels a report using a simple heuristic, which looks for the words and phrases acquired during the semantic lexicon learning process in the report. The second approach recasts cause identification as a text classification problem, employing supervised and transductive text classification algorithms to learn models from incident reports labeled with shaping factors and using the models to label unseen reports. Our experiments show that both the heuristic-based approach and the learning-based approach (when given sufficient training data) outperform the baseline system significantly.
]]></description>
  <author>M.  A. Abedin, V.  Ng and L.  Khan</author>
  <category>NLP</category>
  <comments>http://www.jair.org/comments.html</comments>
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  <pubDate>Thu, 26 Aug 2010 20:05:34 -0500</pubDate>
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<item>
  <title>Non-Transferable Utility Coalitional Games via Mixed-Integer Linear Constraints</title>
  <link>http://www.jair.org/papers/paper3060.html</link>
  <description><![CDATA[Coalitional games serve the purpose of modeling payoff distribution problems in scenarios where agents can collaborate by forming coalitions in order to obtain higher worths than by acting in isolation. In the classical Transferable Utility (TU) setting, coalition worths can be freely distributed amongst agents. However, in several application scenarios, this is not the case and the Non-Transferable Utility setting (NTU) must be considered, where additional application-oriented constraints are imposed on the possible worth distributions.
<br /><br />
In this paper, an approach to define NTU games is proposed which is based on describing allowed distributions via a set of mixed-integer linear constraints applied to an underlying TU game. It is shown that such games allow non-transferable conditions on worth distributions to be specified in a natural and succinct way. The properties and the relationships among the most prominent solution concepts for NTU games that hold when they are applied on (mixed-integer) constrained games are investigated. Finally, a thorough analysis is carried out to assess the impact of issuing constraints on the computational complexity of some of these solution concepts.]]></description>
  <author>G.  Greco, E.  Malizia, L.  Palopoli and F.  Scarcello</author>
  <category>MAS/Cecon</category>
  <comments>http://www.jair.org/comments.html</comments>
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  <pubDate>Thu, 26 Aug 2010 21:34:14 -0500</pubDate>
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<item>
  <title>Automatic Induction of Bellman-Error Features for Probabilistic Planning</title>
  <link>http://www.jair.org/papers/paper3021.html</link>
  <description><![CDATA[Domain-specific features are important in representing problem structure throughout machine learning and decision-theoretic planning. In planning, once state features are provided, domain-independent 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).]]></description>
  <author>J.  Wu and R.  Givan</author>
  <category>Plan/Sched</category>
  <comments>http://www.jair.org/comments.html</comments>
  <enclosure url='http://www.jair.org/media/3021/live-3021-5147-jair.pdf' length='569741' type='application/pdf' />
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  <pubDate>Mon, 30 Aug 2010 00:00:00 -0500</pubDate>
  <source url='http://www.jair.org/articles.rss'>Journal of Artificial Intelligence Research</source>
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