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A Salido M., “Robustness, Stability, Recoverability and Reliability in Dynamic Constraint Satisfaction Problems”, Technical Report DSICIAPS: 1, Departamento de Sistemas Informaticos y Computacion. Universidad Politecnica de
, 2011
"... Abstract. 1 Many realworld problems in Artificial Intelligence (AI) as well as ..."
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Cited by 1 (0 self)
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Abstract. 1 Many realworld problems in Artificial Intelligence (AI) as well as
FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem
 In Proceedings of the AAAI National Conference on Artificial Intelligence
, 2002
"... The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filterbase ..."
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Cited by 599 (10 self)
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The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter
The Byzantine Generals Problem,"
 ACM Transactions on Programming Languages and Systems,
, 1982
"... Abstract The Byzantine Generals Problem requires processes to reach agreement upon a value even though some of them may fad. It is weakened by allowing them to agree upon an "incorrect" value if a failure occurs. The transaction eormmt problem for a distributed database Js a special case ..."
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Cited by 1561 (6 self)
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of the weaker problem. It is shown that, like the original Byzantine Generals Problem, the weak version can be solved only ff fewer than onethird of the processes may fad. Unlike the onginal problem, an approximate solution exists that can tolerate arbaranly many failures.
Where the REALLY Hard Problems Are
 IN J. MYLOPOULOS AND R. REITER (EDS.), PROCEEDINGS OF 12TH INTERNATIONAL JOINT CONFERENCE ON AI (IJCAI91),VOLUME 1
, 1991
"... It is well known that for many NPcomplete problems, such as KSat, etc., typical cases are easy to solve; so that computationally hard cases must be rare (assuming P != NP). This paper shows that NPcomplete problems can be summarized by at least one "order parameter", and that the hard p ..."
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Cited by 683 (1 self)
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It is well known that for many NPcomplete problems, such as KSat, etc., typical cases are easy to solve; so that computationally hard cases must be rare (assuming P != NP). This paper shows that NPcomplete problems can be summarized by at least one "order parameter", and that the hard
ANALYSIS OF WIRELESS SENSOR NETWORKS FOR HABITAT MONITORING
, 2004
"... We provide an indepth study of applying wireless sensor networks (WSNs) to realworld habitat monitoring. A set of system design requirements were developed that cover the hardware design of the nodes, the sensor network software, protective enclosures, and system architecture to meet the require ..."
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Cited by 1490 (19 self)
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the requirements of biologists. In the summer of 2002, 43 nodes were deployed on a small island off the coast of Maine streaming useful live data onto the web. Although researchers anticipate some challenges arising in realworld deployments of WSNs, many problems can only be discovered through experience. We
The Vocabulary Problem in HumanSystem Communication
 COMMUNICATIONS OF THE ACM
, 1987
"... In almost all computer applications, users must enter correct words for the desired objects or actions. For success without extensive training, or in firsttries for new targets, the system must recognize terms that will be chosen spontaneously. We studied spontaneous word choice for objects in five ..."
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Cited by 562 (8 self)
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. For example, the popular approach in which access is via one designer's favorite single word will result in 8090 percent failure rates in many common situations. An optimal strategy, unlimited aliasing, is derived and shown to be capable of severalfold improvements.
Multitask Learning,”
, 1997
"... Abstract. Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for ..."
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Cited by 677 (6 self)
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, and sketch an algorithm for multitask learning in decision trees. Because multitask learning works, can be applied to many different kinds of domains, and can be used with different learning algorithms, we conjecture there will be many opportunities for its use on realworld problems.
Bayesian Network Learning with Parameter Constraints
, 2006
"... The task of learning models for many realworld problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of training data. ..."
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Cited by 34 (2 self)
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The task of learning models for many realworld problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of training data.
Finitetime analysis of the multiarmed bandit problem
 Machine Learning
, 2002
"... Abstract. Reinforcement learning policies face the exploration versus exploitation dilemma, i.e. the search for a balance between exploring the environment to find profitable actions while taking the empirically best action as often as possible. A popular measure of a policy’s success in addressing ..."
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Cited by 817 (15 self)
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this dilemma is the regret, that is the loss due to the fact that the globally optimal policy is not followed all the times. One of the simplest examples of the exploration/exploitation dilemma is the multiarmed bandit problem. Lai and Robbins were the first ones to show that the regret for this problem has
MultiPlayer Game Approach to Solving MultiEntity Problems
"... Many realworld problems involve the allocation of limited resources to competing consumers with dissimilar objective functions. Current techniques that handle such problems usually examine the problem domain as a whole ..."
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Many realworld problems involve the allocation of limited resources to competing consumers with dissimilar objective functions. Current techniques that handle such problems usually examine the problem domain as a whole
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