From jair-ed at ISI.EDU Wed Jul 2 11:46:05 2008
From: jair-ed at ISI.EDU (jair-ed@ISI.EDU)
Date: Wed, 02 Jul 2008 18:46:05 +0000
Subject: [Jairsubscribers] 7 new articles published by JAIR
Message-ID:
Dear JAIR subscriber:
We are pleased to announce that the JAIR mailing list is back in operation.
This message lists papers that have been published in JAIR in the last month and describes how to access them. (If you wish to remove yourself from this mailing list, see instructions at the end of this message.)
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I. New JAIR Articles
V. Bulitko, M. Lustrek, J. Schaeffer, Y. Bjornsson and S. Sigmundarson (2008)
"Dynamic Control in Real-Time Heuristic Search",
Volume 32, pages 419-452
For quick access go to
Abstract:
Real-time heuristic search is a challenging type of agent-centered search because the agent's planning time per action is bounded by a constant independent of problem size. A common problem that imposes such restrictions is pathfinding in modern computer games where a large number of units must plan their paths simultaneously over large maps. Common search algorithms (e.g., A*, IDA*, D*, ARA*, AD*) are inherently not real-time and may lose completeness when a constant bound is imposed on per-action planning time. Real-time search algorithms retain completeness but frequently produce unacceptably suboptimal solutions. In this paper, we extend classic and modern real-time search algorithms with an automated mechanism for dynamic depth and subgoal selection. The new algorithms remain real-time and complete. On large computer game maps, they find paths within 7% of optimal while on average expanding roughly a single state per action. This is nearly a three-fold improvement in suboptimality over the existing state-of-the-art algorithms and, at the same time, a 15-fold improvement in the amount of planning per action.
B. C. Csaji and L. Monostori (2008)
"Adaptive Stochastic Resource Control: A Machine Learning Approach",
Volume 32, pages 453-486
For quick access go to
Abstract:
The paper investigates stochastic resource allocation problems with scarce, reusable resources and non-preemtive, time-dependent, interconnected tasks. This approach is a natural generalization of several standard resource management problems, such as scheduling and transportation problems. First, reactive solutions are considered and defined as control policies of suitably reformulated Markov decision processes (MDPs). We argue that this reformulation has several favorable properties, such as it has finite state and action spaces, it is aperiodic, hence all policies are proper and the space of control policies can be safely restricted. Next, approximate dynamic programming (ADP) methods, such as fitted Q-learning, are suggested for computing an efficient control policy. In order to compactly maintain the cost-to-go function, two representations are studied: hash tables and support vector regression (SVR), particularly, nu-SVRs. Several additional improvements, such as the application of limited-lookahead rollout algorithms in the initial phases, action space decomposition, task clustering and distributed sampling are investigated, too. Finally, experimental results on both benchmark and industry-related data are presented.
F. Stulp and M. Beetz (2008)
"Refining the Execution of Abstract Actions with Learned Action Models",
Volume 32, pages 487-523
For quick access go to
Abstract:
Robots reason about abstract actions, such as "go to position `l'", in order to decide what to do or to generate plans for their intended course of action. The use of abstract actions enables robots to employ small action libraries, which reduces the search space for decision making. When executing the actions, however, the robot must tailor the abstract actions to the specific task and situation context at hand.
In this article we propose a novel robot action execution system that learns success and performance models for possible specializations of abstract actions. At execution time, the robot uses these models to optimize the execution of abstract actions to the respective task contexts. The robot can so use abstract actions for efficient reasoning, without compromising the performance of action execution. We show the impact of our action execution model in three robotic domains and on two kinds of action execution problems: (1) the instantiation of free action parameters to optimize the expected performance of action sequences; (2) the automatic introduction of additional subgoals to make action sequences more reliable.
S. Bouveret and J. Lang (2008)
"Efficiency and Envy-freeness in Fair Division of Indivisible Goods: Logical Representation and Complexity",
Volume 32, pages 525-564
For quick access go to
Abstract:
We consider the problem of allocating fairly a set of indivisible goods among agents from the point of view of compact representation and computational complexity. We start by assuming that agents have dichotomous preferences expressed by propositional formulae. We express efficiency and envy-freeness in a logical setting, which reveals unexpected connections to nonmonotonic reasoning. Then we identify the complexity of determining whether there exists an efficient and envy-free allocation, for several notions of efficiency, when preferences are represented in a succinct way (as well as restrictions of this problem). We first study the problem under the assumption that preferences are dichotomous, and then in the general case.
L. Xu, F. Hutter, H. H. Hoos and K. Leyton-Brown (2008)
"SATzilla: Portfolio-based Algorithm Selection for SAT",
Volume 32, pages 565-606
For quick access go to
Abstract:
It has been widely observed that there is no single "dominant" SAT solver; instead, different solvers perform best on different instances. Rather than following the traditional approach of choosing the best solver for a given class of instances, we advocate making this decision online on a per-instance basis. Building on previous work, we describe SATzilla, an automated approach for constructing per-instance algorithm portfolios for SAT that use so-called empirical hardness models to choose among their constituent solvers. This approach takes as input a distribution of problem instances and a set of component solvers, and constructs a portfolio optimizing a given objective function (such as mean runtime, percent of instances solved, or score in a competition). The excellent performance of SATzilla was independently verified in the 2007 SAT Competition, where our SATzilla07 solvers won three gold, one silver and one bronze medal. In this article, we go well beyond SATzilla07 by making the portfolio construction scalable and completely automated, and improving it by integrating local search solvers as candidate solvers, by predicting performance score instead of runtime, and by using hierarchical hardness models that take into account different types of SAT instances. We demonstrate the effectiveness of these new techniques in extensive experimental results on data sets including instances from the most recent SAT competition.
L. Bordeaux, M. Cadoli and T. Mancini (2008)
"A Unifying Framework for Structural Properties of CSPs: Definitions, Complexity, Tractability",
Volume 32, pages 607-629
For quick access go to
Abstract:
Literature on Constraint Satisfaction exhibits the definition of several ``structural'' properties that can be possessed by CSPs, like (in)consistency, substitutability or interchangeability.
Current tools for constraint solving typically detect such properties efficiently by means of incomplete yet effective algorithms, and use them to reduce the search space and boost search.
In this paper, we provide a unifying framework encompassing most of the properties known so far, both in CSP and other fields' literature, and shed light on the semantical relationships among them.
This gives a unified and comprehensive view of the topic, allows new, unknown, properties to emerge, and clarifies the computational complexity of the various detection problems.
In particular, among the others, two new concepts, fixability and removability emerge, that come out to be the ideal characterisations of values that may be safely assigned or removed from a variable's domain, while preserving problem satisfiability.
These two notions subsume a large number of known properties, including
inconsistency, substitutability and others.
Because of the computational intractability of all the property-detection problems, by following the CSP approach we then determine a number of relaxations which provide sufficient conditions for their tractability. In particular, we exploit forms of language restrictions and local reasoning.
F. Yang , J. Culberson , R. Holte, U. Zahavi and A. Felner (2008)
"A General Theory of Additive State Space Abstractions",
Volume 32, pages 631-662
For quick access go to
Abstract:
Informally, a set of abstractions of a state space S is additive if the distance between any two states in S is always greater than or equal to the sum of the corresponding distances in the abstract spaces. The first known additive abstractions, called disjoint pattern databases, were experimentally demonstrated to produce state of the art performance on certain state spaces. However, previous applications were restricted to state spaces with special properties, which precludes disjoint pattern databases from being defined for several commonly used testbeds, such as Rubik's Cube, TopSpin and the Pancake puzzle. In this paper we give a general definition of additive abstractions that can be applied to any state space and prove that heuristics based on additive abstractions are consistent as well as admissible. We use this new definition to create additive abstractions for these testbeds and show experimentally that well chosen additive abstractions can reduce search time substantially for the (18,4)-TopSpin puzzle and by three orders of magnitude over state of the art methods for the 17-Pancake puzzle. We also derive a way of testing if the heuristic value returned by additive abstractions is provably too low and show that the use of this test can reduce search time for the 15-puzzle and TopSpin by roughly a factor of two.
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II. Unsubscribing from our Mailing List
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articles", enter your email address in the form at the bottom of the
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From jair-ed at ISI.EDU Wed Sep 17 09:07:17 2008
From: jair-ed at ISI.EDU (jair-ed@ISI.EDU)
Date: Wed, 17 Sep 2008 16:07:17 +0000
Subject: [Jairsubscribers] 8 new articles published by JAIR
Message-ID:
Dear JAIR subscriber:
This message lists papers that have been recently published in JAIR and describes how to access them. (If you wish to remove yourself from this mailing list, see instructions at the end of this message.)
----------------------------------------------------------------
I. New JAIR Articles
S. Ross, J. Pineau, S. Paquet and B. Chaib-draa (2008)
"Online Planning Algorithms for POMDPs",
Volume 32, pages 663-704
For quick access go to
Abstract:
Partially Observable Markov Decision Processes (POMDPs) provide a rich framework for sequential decision-making under uncertainty in stochastic domains. However, solving a POMDP is often intractable except for small problems due to their complexity. Here, we focus on online approaches that alleviate the computational complexity by computing good local policies at each decision step during the execution. Online algorithms generally consist of a lookahead search to find the best action to execute at each time step in an environment. Our objectives here are to survey the various existing online POMDP methods, analyze their properties and discuss their advantages and disadvantages; and to thoroughly evaluate these online approaches in different environments under various metrics (return, error bound reduction, lower bound improvement). Our experimental results indicate that state-of-the-art online heuristic search methods can handle large POMDP domains efficiently.
A. Petcu, B. Faltings and D. C. Parkes (2008)
"M-DPOP: Faithful Distributed Implementation of Efficient Social Choice Problems",
Volume 32, pages 705-755
For quick access go to
Abstract:
In the efficient social choice problem, the goal is to assign values, subject to side constraints, to a set of variables to maximize the total utility across a population of agents, where each agent has private information about its utility function. In this paper we model the social choice problem as a distributed constraint optimization problem (DCOP), in which each agent can communicate with other agents that share an interest in one or more variables. Whereas existing DCOP algorithms can be easily manipulated by an agent, either by misreporting private information or deviating from the algorithm, we introduce M-DPOP, the first DCOP algorithm that provides a faithful distributed implementation for efficient social choice. This provides a concrete example of how the methods of mechanism design can be unified with those of distributed optimization. Faithfulness ensures that no agent can benefit by unilaterally deviating from any aspect of the protocol, neither information-revelation, computation, nor communication, and whatever the private information of other agents. We allow for payments by agents to a central bank, which is the only central authoritythat we require. To achieve faithfulness, we carefully integrate the Vickrey-Clarke-Groves (VCG) mechanism with the DPOP algorithm, such that each agent is only asked to perform computation, report information, and send messages that is in its own best interest. Determining agent i's payment requires solving the social choice problem without agent i. Here, we present a method to reuse computation performed in solving the main problem in a way that is robust against manipulation by the excluded agent. Experimental results on structured problems show that as much as 87% of the computation required for solving the marginal problems can be avoided by re-use, providing very good scalability in the number of agents.
On unstructured problems, we observe a sensitivity of M-DPOP to the density of the problem, and we show that reusability decreases from almost 100% for very sparse problems to around 20% for highly connected problems. We close with a discussion of the features of DCOP that enable faithful implementations in this problem, the challenge of reusing computation from the main problem to marginal problems in other algorithms such as ADOPT and OptAPO, and the prospect of methods to avoid the welfare loss that can occur because of the transfer of payments to the bank.
J. Delgrande, Y. Jin and F. J. Pelletier (2008)
"Compositional Belief Update",
Volume 32, pages 757-791
For quick access go to
Abstract:
In this paper we explore a class of belief update operators, in which the definition of the operator is compositional with respect to the sentence to be added. The goal is to provide an update operator that is intuitive, in that its definition is based on a recursive decomposition of the update sentence's structure, and that may be reasonably implemented. In addressing update, we first provide a definition phrased in terms of the models of a knowledge base. While this operator satisfies a core group of the benchmark Katsuno-Mendelzon update postulates, not all of the postulates are satisfied. Other Katsuno-Mendelzon postulates can be obtained by suitably restricting the syntactic form of the sentence for update, as we show. In restricting the syntactic form of the sentence for update, we also obtain a hierarchy of update operators with Winslett's standard semantics as the most basic interesting approach captured. We subsequently give an algorithm which captures this approach; in the general case the algorithm is exponential, but with some not-unreasonable assumptions we obtain an algorithm that is linear in the size of the knowledge base. Hence the resulting approach has much better complexity characteristics than other operators in some situations. We also explore other compositional belief change operators: erasure is developed as a dual operator to update; we show that a forget operator is definable in terms of update; and we give a definition of the compositional revision operator. We obtain that compositional revision, under the most natural definition, yields the Satoh revision operator.
L. Miclet, S. Bayoudh and A. Delhay (2008)
"Analogical Dissimilarity: Definition, Algorithms and Two Experiments in Machine Learning",
Volume 32, pages 793-824
For quick access go to
Abstract:
This paper defines the notion of analogical dissimilarity between four objects, with a special focus on objects structured as sequences. Firstly, it studies the case where the four objects have a null analogical dissimilarity, i.e. are in analogical proportion. Secondly, when one of these objects is unknown, it gives algorithms to compute it. Thirdly, it tackles the problem of defining analogical dissimilarity, which is a measure of how far four objects are from being in analogical proportion. In particular, when objects are sequences, it gives a definition and an algorithm based on an optimal alignment of the four sequences. It gives also learning algorithms, i.e. methods to find the triple of objects in a learning sample which has the least analogical dissimilarity with a given object. Two practical experiments are described: the first is a classification problem on benchmarks of binary and nominal data, the second shows how the generation of sequences by solving analogical equations enables a handwritten character recognition system to rapidly be adapted to a new writer.
G. M. Coghill, A. Srinivasan and R. D. King (2008)
"Qualitative System Identification from Imperfect Data",
Volume 32, pages 825-877
For quick access go to
Abstract:
Experience in the physical sciences suggests that the only realistic means of understanding complex systems is through the use of mathematical models. Typically, this has come to mean the identification of quantitative models expressed as differential equations. Quantitative modelling works best when the structure of the model (i.e., the form of the equations) is known; and the primary concern is one of estimating the values of the parameters in the model. For complex biological systems, the model-structure is rarely known and the modeler has to deal with both model-identification and parameter-estimation. In this paper we are concerned with providing automated assistance to the first of these problems. Specifically, we examine the identification by machine of the structural relationships between experimentally observed variables. These relationship will be expressed in the form of qualitative abstractions of a quantitative model. Such qualitative models may not only provide clues to the precise quantitative model, but also assist in understanding the essence of that model. Our position in this paper is that background knowledge incorporating system modelling principles can be used to constrain effectively the set of good qualitative models. Utilising the model-identification framework provided by Inductive Logic Programming (ILP) we present empirical support for this position using a series of increasingly complex artificial datasets. The results are obtained with qualitative and quantitative data subject to varying amounts of noise and different degrees of sparsity. The results also point to the presence of a set of qualitative states, which we term kernel subsets, that may be necessary for a qualitative model-learner to learn correct models. We demonstrate scalability of the method to biological system modelling by identification of the glycolysis metabolic pathway from data.
Y. Wang, N. L. Zhang and T. Chen (2008)
"Latent Tree Models and Approximate Inference in Bayesian Networks",
Volume 32, pages 879-900
For quick access go to
Abstract:
We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and when online, make inference with the LTM instead of the original BN. Because LTMs are tree-structured, inference takes linear time. In the meantime, they can represent complex relationship among leaf nodes and hence the approximation accuracy is often good. Empirical evidence shows that our method can achieve good approximation accuracy at low online computational cost.
N. C. A. Moore and P. Prosser (2008)
"The Ultrametric Constraint and its Application to Phylogenetics",
Volume 32, pages 901-938
For quick access go to
Abstract:
A phylogenetic tree shows the evolutionary relationships among species. Internal nodes of the tree represent speciation events and leaf nodes correspond to species. A goal of phylogenetics is to combine such trees into larger trees, called supertrees, whilst respecting the relationships in the original trees. A rooted tree exhibits an ultrametric property; that is, for any three leaves of the tree it must be that one pair has a deeper most recent common ancestor than the other pairs, or that all three have the same most recent common ancestor. This inspires a constraint programming encoding for rooted trees. We present an efficient constraint that enforces the ultrametric property over a symmetric array of constrained integer variables, with the inevitable property that the lower bounds of any three variables are mutually supportive. We show that this allows an efficient constraint-based solution to the supertree construction problem. We demonstrate that the versatility of constraint programming can be exploited to allow solutions to variants of the supertree construction problem.
E. H. Gerding, R. K. Dash, A. Byde and N. R. Jennings (2008)
"Optimal Strategies for Simultaneous Vickrey Auctions with Perfect Substitutes",
Volume 32, pages 939-982
For quick access go to
Abstract:
We derive optimal strategies for a bidding agent that participates in multiple, simultaneous second-price auctions with perfect substitutes. We prove that, if everyone else bids locally in a single auction, the global bidder should always place non-zero bids in all available auctions, provided there are no budget constraints. With a budget, however, the optimal strategy is to bid locally if this budget is equal or less than the valuation. Furthermore, for a wide range of valuation distributions, we prove that the problem of finding the optimal bids reduces to two dimensions if all auctions are identical. Finally, we address markets with both sequential and simultaneous auctions, non-identical auctions, and the allocative efficiency of the market.
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II. Unsubscribing from our Mailing List
To remove yourself from the JAIR subscribers mailing list, visit our
Web site (http://www.jair.org/), follow the link "notify me of new
articles", enter your email address in the form at the bottom of the
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From jair-ed at ISI.EDU Wed Oct 1 10:41:11 2008
From: jair-ed at ISI.EDU (jair-ed@ISI.EDU)
Date: Wed, 01 Oct 2008 17:41:11 +0000
Subject: [Jairsubscribers] 5 new articles published by JAIR
Message-ID:
Dear JAIR subscriber:
This message lists papers that have been recently published in JAIR and describes how to access them. (If you wish to remove yourself from this mailing list, see instructions at the end of this message.)
----------------------------------------------------------------
I. New JAIR Articles
S. Esmeir and S. Markovitch (2008)
"Anytime Induction of Low-cost, Low-error Classifiers: a Sampling-based Approach",
Volume 33, pages 1-31
For quick access go to
Abstract:
Machine learning techniques are gaining prevalence in the production of a wide range of classifiers for complex real-world applications with nonuniform testing and misclassification costs. The increasing complexity of these applications poses a real challenge to resource management during learning and classification. In this work we introduce ACT (anytime cost-sensitive tree learner), a novel framework for operating in such complex environments. ACT is an anytime algorithm that allows learning time to be increased in return for lower classification costs. It builds a tree top-down and exploits additional time resources to obtain better estimations for the utility of the different candidate splits. Using sampling techniques, ACT approximates the cost of the subtree under each candidate split and favors the one with a minimal cost. As a stochastic algorithm, ACT is expected to be able to escape local minima, into which greedy methods may be trapped. Experiments with a variety of datasets were conducted to compare ACT to the state-of-the-art cost-sensitive tree learners. The results show that for the majority of domains ACT produces significantly less costly trees. ACT also exhibits good anytime behavior with diminishing returns.
B. Lubin, A. I. Juda, R. Cavallo, S. Lahaie, J. Shneidman and D. C. Parkes (2008)
"ICE: An Expressive Iterative Combinatorial Exchange",
Volume 33, pages 33-77
For quick access go to
Abstract:
We present the design and analysis of the first fully expressive, iterative combinatorial exchange (ICE). The exchange incorporates a tree-based bidding language (TBBL) that is concise and expressive for CEs. Bidders specify lower and upper bounds in TBBL on their value for different trades and refine these bounds across rounds. These bounds allow price discovery and useful preference elicitation in early rounds, and allow termination with an efficient trade despite partial information on bidder valuations. All computation in the exchange is carefully optimized to exploit the structure of the bid-trees and to avoid enumerating trades. A proxied interpretation of a revealed-preference activity rule, coupled with simple linear prices, ensures progress across rounds. The exchange is fully implemented, and we give results demonstrating several aspects of its scalability and economic properties with simulated bidding strategies.
D. Martinez, O. Lopez de Lacalle and E. Agirre (2008)
"On the Use of Automatically Acquired Examples for All-Nouns Word Sense Disambiguation ",
Volume 33, pages 79-107
For quick access go to
Abstract:
This article focuses on Word Sense Disambiguation (WSD), which is a Natural Language Processing task that is thought to be important for many Language Technology applications, such as Information Retrieval, Information Extraction, or Machine Translation. One of the main issues preventing the deployment of WSD technology is the lack of training examples for Machine Learning systems, also known as the Knowledge Acquisition Bottleneck. A method which has been shown to work for small samples of words is the automatic acquisition of examples. We have previously shown that one of the most promising example acquisition methods scales up and produces a freely available database of 150 million examples from Web snippets for all polysemous nouns in WordNet. This paper focuses on the issues that arise when using those examples, all alone or in addition to manually tagged examples, to train a supervised WSD system for all nouns. The extensive evaluation on both lexical-sample and all-words Senseval benchmarks shows that we are able to improve over commonly used baselines and to achieve top-rank performance. The good use of the prior distributions from the senses proved to be a crucial factor.
Y. Gal and A. Pfeffer (2008)
"Networks of Influence Diagrams: A Formalism for Representing Agents' Beliefs and Decision-Making Processes",
Volume 33, pages 109-147
For quick access go to
Abstract:
This paper presents Networks of Influence Diagrams (NID), a compact, natural and highly expressive language for reasoning about agents' beliefs and decision-making processes. NIDs are graphical structures in which agents' mental models are represented as nodes in a network; a mental model for an agent may itself use descriptions of the mental models of other agents. NIDs are demonstrated by examples, showing how they can be used to describe conflicting and cyclic belief structures, and certain forms of bounded rationality. In an opponent modeling domain, NIDs were able to outperform other computational agents whose strategies were not known in advance. NIDs are equivalent in representation to Bayesian games but they are more compact and structured than this formalism. In particular, the equilibrium definition for NIDs makes an explicit distinction between agents' optimal strategies, and how they actually behave in reality.
R. Meir, A. D. Procaccia, J. S. Rosenschein and Aviv Zohar (2008)
"Complexity of Strategic Behavior in Multi-Winner Elections",
Volume 33, pages 149-178
For quick access go to
Abstract:
Although recent years have seen a surge of interest in the computational aspects of social choice, no specific attention has previously been devoted to elections with multiple winners, e.g., elections of an assembly or committee. In this paper, we characterize the worst-case complexity of manipulation and control in the context of four prominent multi-winner voting systems, under different formulations of the strategic agent???s goal.
----------------------------------------------------------------
II. Unsubscribing from our Mailing List
To remove yourself from the JAIR subscribers mailing list, visit our
Web site (http://www.jair.org/), follow the link "notify me of new
articles", enter your email address in the form at the bottom of the
page, and follow the directions. In the event that you've already
deleted yourself from the list and we keep sending you messages like
this one, send mail to jair-ed at isi.edu.
----------------------------------------------------------------
From jair-ed at ISI.EDU Sun Nov 30 15:45:52 2008
From: jair-ed at ISI.EDU (jair-ed@ISI.EDU)
Date: Sun, 30 Nov 2008 23:45:52 +0000
Subject: [Jairsubscribers] 8 new articles published by JAIR (Sept-Oct 2008)
Message-ID:
Dear JAIR subscriber:
This message lists papers that have been recently published in JAIR and describes how to access them. (If you wish to remove yourself from this mailing list, see instructions at the end of this message.)
----------------------------------------------------------------
I. New JAIR Articles
S. Esmeir and S. Markovitch (2008)
"Anytime Induction of Low-cost, Low-error Classifiers: a Sampling-based Approach",
Volume 33, pages 1-31
For quick access go to
Abstract:
Machine learning techniques are gaining prevalence in the production of a wide range of classifiers for complex real-world applications with nonuniform testing and misclassification costs. The increasing complexity of these applications poses a real challenge to resource management during learning and classification. In this work we introduce ACT (anytime cost-sensitive tree learner), a novel framework for operating in such complex environments. ACT is an anytime algorithm that allows learning time to be increased in return for lower classification costs. It builds a tree top-down and exploits additional time resources to obtain better estimations for the utility of the different candidate splits. Using sampling techniques, ACT approximates the cost of the subtree under each candidate split and favors the one with a minimal cost. As a stochastic algorithm, ACT is expected to be able to escape local minima, into which greedy methods may be trapped. Experiments with a variety of datasets were conducted to compare ACT to the state-of-the-art cost-sensitive tree learners. The results show that for the majority of domains ACT produces significantly less costly trees. ACT also exhibits good anytime behavior with diminishing returns.
B. Lubin, A. I. Juda, R. Cavallo, S. Lahaie, J. Shneidman and D. C. Parkes (2008)
"ICE: An Expressive Iterative Combinatorial Exchange",
Volume 33, pages 33-77
For quick access go to
Abstract:
We present the design and analysis of the first fully expressive, iterative combinatorial exchange (ICE). The exchange incorporates a tree-based bidding language (TBBL) that is concise and expressive for CEs. Bidders specify lower and upper bounds in TBBL on their value for different trades and refine these bounds across rounds. These bounds allow price discovery and useful preference elicitation in early rounds, and allow termination with an efficient trade despite partial information on bidder valuations. All computation in the exchange is carefully optimized to exploit the structure of the bid-trees and to avoid enumerating trades. A proxied interpretation of a revealed-preference activity rule, coupled with simple linear prices, ensures progress across rounds. The exchange is fully implemented, and we give results demonstrating several aspects of its scalability and economic properties with simulated bidding strategies.
D. Martinez, O. Lopez de Lacalle and E. Agirre (2008)
"On the Use of Automatically Acquired Examples for All-Nouns Word Sense Disambiguation ",
Volume 33, pages 79-107
For quick access go to
Abstract:
This article focuses on Word Sense Disambiguation (WSD), which is a Natural Language Processing task that is thought to be important for many Language Technology applications, such as Information Retrieval, Information Extraction, or Machine Translation. One of the main issues preventing the deployment of WSD technology is the lack of training examples for Machine Learning systems, also known as the Knowledge Acquisition Bottleneck. A method which has been shown to work for small samples of words is the automatic acquisition of examples. We have previously shown that one of the most promising example acquisition methods scales up and produces a freely available database of 150 million examples from Web snippets for all polysemous nouns in WordNet. This paper focuses on the issues that arise when using those examples, all alone or in addition to manually tagged examples, to train a supervised WSD system for all nouns. The extensive evaluation on both lexical-sample and all-words Senseval benchmarks shows that we are able to improve over commonly used baselines and to achieve top-rank performance. The good use of the prior distributions from the senses proved to be a crucial factor.
Y. Gal and A. Pfeffer (2008)
"Networks of Influence Diagrams: A Formalism for Representing Agents' Beliefs and Decision-Making Processes",
Volume 33, pages 109-147
For quick access go to
Abstract:
This paper presents Networks of Influence Diagrams (NID), a compact, natural and highly expressive language for reasoning about agents' beliefs and decision-making processes. NIDs are graphical structures in which agents' mental models are represented as nodes in a network; a mental model for an agent may itself use descriptions of the mental models of other agents. NIDs are demonstrated by examples, showing how they can be used to describe conflicting and cyclic belief structures, and certain forms of bounded rationality. In an opponent modeling domain, NIDs were able to outperform other computational agents whose strategies were not known in advance. NIDs are equivalent in representation to Bayesian games but they are more compact and structured than this formalism. In particular, the equilibrium definition for NIDs makes an explicit distinction between agents' optimal strategies, and how they actually behave in reality.
R. Meir, A. D. Procaccia, J. S. Rosenschein and Aviv Zohar (2008)
"Complexity of Strategic Behavior in Multi-Winner Elections",
Volume 33, pages 149-178
For quick access go to
Abstract:
Although recent years have seen a surge of interest in the computational aspects of social choice, no specific attention has previously been devoted to elections with multiple winners, e.g., elections of an assembly or committee. In this paper, we characterize the worst-case complexity of manipulation and control in the context of four prominent multi-winner voting systems, under different formulations of the strategic agent???s goal.
T. De Laet, J. De Schutter and H. Bruyninckx (2008)
"A Rigorously Bayesian Beam Model and an Adaptive Full Scan Model for Range Finders in Dynamic Environments",
Volume 33, pages 179-222
For quick access go to
Abstract:
This paper proposes and experimentally validates a Bayesian network model of a range finder adapted to dynamic environments. All modeling assumptions are rigorously explained, and all model parameters have a physical interpretation. This approach results in a transparent and intuitive model. With respect to the state of the art beam model this paper: (i) proposes a different functional form for the probability of range measurements caused by unmodeled objects, (ii) intuitively explains the discontinuity encountered in te state of the art beam model, and (iii) reduces the number of model parameters, while maintaining the same representational power for experimental data. The proposed beam model is called RBBM, short for Rigorously Bayesian Beam Model. A maximum likelihood and a variational Bayesian estimator (both based on expectation-maximization) are proposed to learn the model parameters.
Furthermore, the RBBM is extended to a full scan model in two steps: first, to a full scan model for static environments and next, to a full scan model for general, dynamic environments. The full scan model accounts for the dependency between beams and adapts to the local sample density when using a particle filter. In contrast to Gaussian-based state of the art models, the proposed full scan model uses a sample-based approximation. This sample-based approximation enables handling dynamic environments and capturing multi-modality, which occurs even in simple static environments.
T. Grinshpoun and A. Meisels (2008)
"Completeness and Performance Of The APO Algorithm",
Volume 33, pages 223-258
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Abstract:
Asynchronous Partial Overlay (APO) is a search algorithm that uses cooperative mediation to solve Distributed Constraint Satisfaction Problems (DisCSPs). The algorithm partitions the search into different subproblems of the DisCSP. The original proof of completeness of the APO algorithm is based on the growth of the size of the subproblems. The present paper demonstrates that this expected growth of subproblems does not occur in some situations, leading to a termination problem of the algorithm. The problematic parts in the APO algorithm that interfere with its completeness are identified and necessary modifications to the algorithm that fix these problematic parts are given. The resulting version of the algorithm, Complete Asynchronous Partial Overlay (CompAPO), ensures its completeness. Formal proofs for the soundness and completeness of CompAPO are given. A detailed performance evaluation of CompAPO comparing it to other DisCSP algorithms is presented, along with an extensive experimental evaluation of the algorithm?s unique behavior. Additionally, an optimization version of the algorithm, CompOptAPO, is presented, discussed, and evaluated.
I. Rezek, D. S. Leslie, S. Reece, S. J. Roberts, A. Rogers, R. K. Dash and N. R. Jennings (2008)
"On Similarities between Inference in Game Theory and Machine Learning",
Volume 33, pages 259-283
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Abstract:
In this paper, we elucidate the equivalence between inference in game theory and machine learning. Our aim in so doing is to establish an equivalent vocabulary between the two domains so as to facilitate developments at the intersection of both ﬁelds, and as proof of the usefulness of this approach, we use recent developments in each ﬁeld to make useful improvements to the other. More speciﬁcally, we consider the analogies between smooth best responses in ﬁctitious play and Bayesian inference methods. Initially, we use these insights to develop and demonstrate an improved algorithm for learning in games based on probabilistic moderation. That is, by integrating over the distribution of opponent strategies (a Bayesian approach within machine learning) rather than taking a simple empirical average (the approach used in standard ﬁctitious play) we derive a novel moderated ﬁctitious play algorithm and show that it is more likely than standard ﬁctitious play to converge to a payoff-dominant but risk-dominated Nash equilibrium in a simple coordination game. Furthermore we consider the converse case, and show how insights from game theory can be used to derive two improved mean ﬁeld variational learning algorithms. We ﬁrst show that the standard update rule of mean ﬁeld variational learning is analogous to a Cournot adjustment within game theory. By analogy with ﬁctitious play, we then suggest an improved update rule, and show that this results in ﬁctitious variational play, an improved mean ﬁeld variational learning algorithm that exhibits better convergence in highly or strongly connected graphical models. Second, we use a recent advance in ﬁctitious play, namely dynamic ﬁctitious play, to derive a derivative action variational learning algorithm, that exhibits superior convergence properties on a canonical machine learning problem (clustering a mixture distribution).
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