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Proceedings of the National Academy of Sciences of the United States of America, Vol. 101, No. 46. (16 November 2004), pp. 16385-16389.
Abstract
10.1073/pnas.0403723101 We introduce a general framework for modeling functionally diverse problem-solving agents. In this framework, problem-solving agents possess representations of problems and algorithms that they use to locate solutions. We use this framework to establish a result relevant to group composition. We find that when selecting a problem-solving team from a diverse population of intelligent agents, a team of randomly selected agents outperforms a team comprised of the best-performing agents. This result relies on the intuition that, as the initial pool ...
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In CLADE '03: Proceedings of the International Workshop on Challenges of Large Applications in Distributed Environments (2003)
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In Data Mining and Knowledge Discovery Handbook: A Complete Guide for Practitioners and Researchers (2005)
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Evolutionary Computation, 2005. The 2005 IEEE Congress on In Evolutionary Computation, 2005. The 2005 IEEE Congress on, Vol. 3 (2005), pp. 2600-2606 Vol. 3.
Abstract
We propose a new mutation operator - the biased mutation operator (BMO) -for evolution strategies, which is capable of handling problems for constrained fitness landscapes. The idea of our approach is to bias the mutation ellipsoid in relation to the parent and therefore lead the mutations into a beneficial direction self-adaptively. This helps to improve the success rate to reproduce better offspring. Experimental results show this bias enhances the solution quality within constrained search domains. The number of the additional strategy ...
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CoRR, Vol. abs/cs/0602053 (2006)
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In SODA '06: Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm (2006), pp. 937-943.
Abstract
We consider "online bandit geometric optimization," a problem of iterated decision making in a largely unknown and constantly changing environment. The goal is to minimize "regret," defined as the difference between the actual loss of an online decision-making procedure and that of the best single decision in hindsight . "Geometric optimization" refers to a generalization of the well-known multi-armed bandit problem, in which the decision space is some bounded subset of R d , the adversary is restricted to linear ...
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Machine Learning, Vol. 51, No. 3. (2003), pp. 239-261.
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Information Theory, IEEE Transactions on In Information Theory, IEEE Transactions on, Vol. 53, No. 5. (2007), pp. 1866-1872.
Abstract
A simple on-line procedure is considered for the prediction of a real-valued sequence. The algorithm is based on a combination of several simple predictors. If the sequence is a realization of an unbounded stationary and ergodic random process then the average of squared errors converges, almost surely, to that of the optimum, given by the Bayes predictor. An analog result is offered for the classification of binary processes ...
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Machine Learning, Vol. 66, No. 2-3. (March 2007), pp. 321-352.
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IEEE Trans. Comput., Vol. 37, No. 12. (December 1988), pp. 1665-1676.
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(2002)
Abstract
The performance of anytime algorithms having a non-deterministic nature can be improved by solving simultaneously several instances of the algorithm-problem pairs. These pairs may include different instances of a problem (like starting from a different initial state), different algorithms (if several alternatives exist), or several instances of the same algorithm (for non-deterministic algorithms). In this paper we present a general framework for optimal parallelization of independent processes. We show a mathematical model for this framework, present algorithms for optimal scheduling, and ...
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In Artificial General Intelligence (2006), pp. 119-226.
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In Proceedings of Conference on Uncertainty in Artificial Intelligence (1989), pp. 182-193.
Abstract
We introduce a graceful approach to probabilistic inference called Bounded Conditioning. Bounded Conditioning monotonically refines the bounds on posterior probabilities in a belief network with computation, and converges on final probabilities of interest with the allocation of a complete resource fraction. The approach allows a reasoner to exchange arbitrary quantities of computational resource for incremental gains in inference quality. As such, Bounded Conditioning holds promise as a useful inference technique for reasoning under the general conditions of uncertain and varying reasoning ...
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In Encyclopedia of Human Computer Interaction (2004), pp. 554-560.
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Journal of Experimental & Theoretical Artificial Intelligence, Vol. 16, No. 2. (2004), pp. 73-105.
Abstract
Choosing the right problem-solving method from available methods is a crucial skill for experts in many areas. We present a technique for automatic selection among methods based on analysis of their past performances. We formalize the statistical problem involved in choosing an efficient method, derive a solution to this problem, and describe a selection algorithm. The algorithm not only chooses among available methods, but also decides when to abandon the chosen method if it takes too much time. ...
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In Artificial Intelligence Planning Systems (1998), pp. 128-136.
Abstract
The choice of an appropriate problem-solving method, from available methods, is a crucial skill for experts in many areas. We describe a technique for the automatic selection among methods, which is based on a statistical analysis of their past performances. We formalize the statistical problem involved in selecting an efficient problem-solving method, derive a solution to this problem, and describe a method-selection algorithm. The algorithm not only chooses among available methods,... ...
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In LICS 2001 Workshop on Theory and Applications of Satisfiability Testing (SAT-2001), Boston, MA, USA, June 2001, Vol. 9 (2001), pp. 344-359.
Abstract
The DPLL procedure is the most popular complete satisfiability (SAT) solver. While its worst case complexity is exponentiol, the actual running time is greatly affected by the ordering of branch variables during the search. Several branching rules have been proposed, but none is the best in all cases. This work investigates the use of automated methods for choosing then most appropriate branching rule at each node in the search tree. We consider a reinforcementlearning approch where a value... ...
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In Formal Methods in Computer Aided Design (FMCAD'07) (2007)
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In Proc.~of the Twenty-Second Conference on Artifical Intelligence~(AAAI '07) (2007), pp. 1152-1157.
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In Principles and Practice of Constraint Programming - CP 2004, 10th International Conference, CP 2004, Toronto, Canada, September 27 - October 1, 2004, Proceedings, Vol. 3258 (2004), pp. 438-452.
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In Principles and Practice of Constraint Programming - CP 2003, 9th International Conference, CP 2003, Kinsale, Ireland, September 29 - October 3, 2003, Proceedings, Vol. 2833 (2003), pp. 899-903.
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In PACT '04: Proceedings of the 13th International Conference on Parallel Architectures and Compilation Techniques (2004), pp. 278-289.
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In International Conference on Computational Science, Vol. 2660 (2003), pp. 749-758.
edited by Peter M. A. Sloot, David Abramson, Alexander V. Bogdanov, et al.Jack Dongarra, Albert Y. Zomaya, Yuri E. Gorbachev, Peter M. A. Sloot, David Abramson, Alexander V. Bogdanov, Jack Dongarra, Albert Y. Zomaya, Yuri E. Gorbachev
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In Ninth International Symposium on Artificial Intelligence and Mathematics (January 2006)
Abstract
We present an approach for improving the performance of combinatorial optimization algorithms by generating an optimal Parallel Portfolio of Algorithms (PPA). A PPA is a collection of diverse algorithms for solving a single problem, all running concurrently on a single processor until a solution is produced. The performance of the portfolio may be controlled by assigning di#erent shares of processor time to each algorithm. We present a method for finding a static PPA, in which the share of ... ...
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In IJCAI (1989), pp. 979-984.
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IEEE Expert, Vol. 7, No. 4. (1992), pp. 58-65.
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Artif. Intell., Vol. 76, No. 1-2. (1995), pp. 35-74.
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In Proceedings of the Seventh National Conference on Artificial Intelligence (1988), pp. 49-54.
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In Proceedings of the 2001 AAAI Fall Symposium Series: Using Uncertainty within Computation, Cape Cod, MA, USA, November 2001 (2001)
Abstract
this paper. However, it is possible to extend these ideas to several other domains that enjoy portfolios of algorithms and recursive algorithms in particular. Promising results have been obtained on the problem of order statistic selection (Lagoudakis & Littman 2000) and on the problem of propositional satisfiability (Lagoudakis & Littman 2001) ...
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In International Conference on Machine Learning (1995), pp. 209-217.
Abstract
Multi-armed bandits may be viewed as decompositionally-structured Markov decision processes (MDP's) with potentially very large state sets. A particularly elegant methodology for computing optimal policies was developed over twenty ago by Gittins [Gittins & Jones, 1974]. Gittins' approach reduces the problem of finding optimal policies for the original MDP to a sequence of low-dimensional stopping problems whose solutions determine the optimal policy through the so-called "Gittins indices."... ...
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In Principles and Practice of Constraint Programming - CP 2002, 8th International Conference, CP 2002, Ithaca, NY, USA, September 9-13, 2002, Proceedings, Vol. 2470 (2002)
Abstract
The time required for a backtracking search procedure to solve a problem can be reduced by employing randomized restart procedures. ...
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Electronic Notes in Discrete Mathematics, Vol. 9 (2001)
Abstract
Before attempting to solve an instance of the satisfiability problem, what can we ascertain about the instance at hand and how can we put that information to use when selecting and tuning a SAT algorithm to solve the instance? We argue for an ensemble-based approach and describe an illustrative example of how such a methodology can be applied to determine optimal restart cutoff points for systematic, backtracking search procedures for SAT. We discuss the methodology and indicate how it can be... ...
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In Advances in Neural Information Processing Systems, Vol. 11 (1999), pp. 204-210.
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In CPAIOR (2004), pp. 50-64.
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In Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI04) (2004)
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Computational Intelligence, Vol. 21, No. 4. (2005), pp. 373-387.
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In Proc. 17th International Conf. on Machine Learning (2000), pp. 511-518.
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Artificial Intelligence, Vol. 67, No. 2. (1994), pp. 245-285.
Abstract
We are interested in the problem faced by an agent with limited computational capabilities, embedded in a complex environment with other agents and processes not under its control. Careful management of computational resources is important for complex problem-solving tasks in which the time spent in decision making affects the quality of the responses generated by a system. This paper describes an approach to designing systems that are capable of taking their own computational resources into consideration during planning and problem solving. ...
Note (first note only)
Online version titled "Decision-Theoretic Deliberation Scheduling for Problem Solving in Time-Constrained Environments"
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In ICCP: International Conference on Constraint Programming (CP), Vol. 2470 (2002), pp. 556-572.
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