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55
Compressing pattern databases
 In Proceedings of the Nineteenth National Conference on Artificial Intelligence (AAAI04
, 2004
"... A pattern database (PDB) is a heuristic function implemented as a lookup table that stores the lengths of optimal solutions for subproblem instances. Standard PDBs have a distinct entry in the table for each subproblem instance. In this paper we investigate compressing PDBs by merging several entrie ..."
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Cited by 47 (24 self)
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A pattern database (PDB) is a heuristic function implemented as a lookup table that stores the lengths of optimal solutions for subproblem instances. Standard PDBs have a distinct entry in the table for each subproblem instance. In this paper we investigate compressing PDBs by merging several entries into one, thereby allowing the use of PDBs that exceed available memory in their uncompressed form. We introduce a number of methods for determining which entries to merge and discuss their relative merits. These vary from domainindependent approaches that allow any set of entries in the PDB to be merged, to more intelligent methods that take into account the structure of the problem. The choice of the best compression method is based on domaindependent attributes. We present experimental results on a number of combinatorial problems, including the fourpeg Towers of Hanoi problem, the slidingtile puzzles, and the TopSpin puzzle. For the Towers of Hanoi, we show that the search time can be reduced by up to three orders of magnitude by using compressed PDBs compared to uncompressed PDBs of the same size. More modest improvements were observed for the other domains.
The generalized A* architecture
 Journal of Artificial Intelligence Research
, 2007
"... We consider the problem of computing a lightest derivation of a global structure using a set of weighted rules. A large variety of inference problems in AI can be formulated in this framework. We generalize A * search and heuristics derived from abstractions to a broad class of lightest derivation p ..."
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Cited by 45 (6 self)
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We consider the problem of computing a lightest derivation of a global structure using a set of weighted rules. A large variety of inference problems in AI can be formulated in this framework. We generalize A * search and heuristics derived from abstractions to a broad class of lightest derivation problems. We also describe a new algorithm that searches for lightest derivations using a hierarchy of abstractions. Our generalization of A * gives a new algorithm for searching AND/OR graphs in a bottomup fashion. We discuss how the algorithms described here provide a general architecture for addressing the pipeline problem — the problem of passing information back and forth between various stages of processing in a perceptual system. We consider examples in computer vision and natural language processing. We apply the hierarchical search algorithm to the problem of estimating the boundaries of convex objects in grayscale images and compare it to other search methods. A second set of experiments demonstrate the use of a new compositional model for finding salient curves in images. 1.
Perceptual Components for Context Aware Computing
 UBICOMP 2002, International Conference on Ubiquitous Computing, Goteborg
, 2002
"... In this paper we propose a software architecture for observing and modeling human activity. This architecture is derived from an ontology for context awareness. We propose a model in which a user's context is described by a set of roles and relations. Different configurations of roles and relat ..."
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Cited by 44 (13 self)
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In this paper we propose a software architecture for observing and modeling human activity. This architecture is derived from an ontology for context awareness. We propose a model in which a user's context is described by a set of roles and relations. Different configurations of roles and relations correspond to situations within the context. The components of a context model are used to specify processes for observing activity. The ontology for context modeling is derived from both a bottom up system's perspective and a topdown users' perspective. As we define each element, we describe the corresponding components of a processbased software architecture.
Controlling the Learning Process of RealTime Heuristic Search
, 2003
"... Realtime search provides an attractive framework for intelligent autonomous agents, as it allows us to model an agent's ability to improve its performance through experience. However, the behavior of realtime search agents is far from rational during the learning (convergence) process, in tha ..."
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Cited by 38 (0 self)
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Realtime search provides an attractive framework for intelligent autonomous agents, as it allows us to model an agent's ability to improve its performance through experience. However, the behavior of realtime search agents is far from rational during the learning (convergence) process, in that they fail to balance the efforts to achieve a shortterm goal (i.e., to safely arrive at a goal state in the present problem solving trial) and a longterm goal (to find better solutions through repeated trials). As a remedy, we introduce two techniques for controlling the amount of exploration, both overall and per trial. The weighted realtime search reduces the overall amount of exploration and accelerates convergence. It sacrifices admissibility but provides a nontrivial bound on the converged solution cost. The realtime search with upper bounds insures solution quality in each trial when the state space is undirected. These techniques result in a convergence process more stable compared with that of the Learning RealTime A* algorithm.
Improving the scalability of optimal Bayesian network learning with externalmemory frontier breadthfirst branch and bound search
 IN PROCEEDINGS OF THE 27TH CONFERENCE ON UNCERTAINTY IN ARTIFICIAL INTELLIGENCE
"... Previous work has shown that the problem of learning the optimal structure of a Bayesian network can be formulated as a shortest path finding problem in a graph and solved using A* search. In this paper, we improve the scalability of this approach by developing a memoryefficient heuristic search ..."
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Cited by 15 (8 self)
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Previous work has shown that the problem of learning the optimal structure of a Bayesian network can be formulated as a shortest path finding problem in a graph and solved using A* search. In this paper, we improve the scalability of this approach by developing a memoryefficient heuristic search algorithm for learning the structure of a Bayesian network. Instead of using A*, we propose a frontier breadthfirst branch and bound search that leverages the layered structure of the search graph of this problem so that no more than two layers of the graph, plus solution reconstruction information, need to be stored in memory at a time. To further improve scalability, the algorithm stores most of the graph in external memory, such as hard disk, when it does not fit in RAM. Experimental results show that the resulting algorithm solves significantly larger problems than the current state of the art.
Context Aware Observation of Human Activities
 Proceedings of the IEEE International Conference on Multimedia and Expo, ICME2002
, 2002
"... In this paper we define a framework for context aware observation of human activity. A context is defined as a network of situations. A situation network is interpreted as a specification for a federation of processes to observe humans and their actions. We present a processbased software architect ..."
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Cited by 12 (0 self)
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In this paper we define a framework for context aware observation of human activity. A context is defined as a network of situations. A situation network is interpreted as a specification for a federation of processes to observe humans and their actions. We present a processbased software architecture for building systems for observing acivity and discuss methods for building systems using this framework. The framework and methods are illustrated with examples from observation of human activity in an "Augmented Meeting Environment". 1.
Bestfirst search for treewidth
 In AAAI’07: Proceedings of the 22nd national conference on Artificial intelligence
, 2007
"... Finding the exact treewidth of a graph is central to many operations in a variety of areas, including probabilistic reasoning and constraint satisfaction. Treewidth can be found by searching over the space of vertex elimination orders. This search space differs from those where bestfirst search is ..."
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Cited by 12 (2 self)
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Finding the exact treewidth of a graph is central to many operations in a variety of areas, including probabilistic reasoning and constraint satisfaction. Treewidth can be found by searching over the space of vertex elimination orders. This search space differs from those where bestfirst search is typically applied, because a solution path is evaluated by its maximum edge cost instead of the sum of its edge costs. We show how to make bestfirst search admissible on maxcost problem spaces. We also employ breadthfirst heuristic search to reduce the memory requirement while still eliminating all duplicate nodes in the search space. Our empirical results show that our algorithms find the exact treewidth an order of magnitude faster than the previous stateoftheart algorithm on hard benchmark graphs.
Survey on Directed Model Checking
, 2009
"... Abstract. This article surveys and gives historical accounts to the algorithmic essentials of directed model checking, a promising bughunting technique to mitigate the state explosion problem. In the enumeration process, successor selection is prioritized. We discuss existing guidance and methods t ..."
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Cited by 9 (1 self)
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Abstract. This article surveys and gives historical accounts to the algorithmic essentials of directed model checking, a promising bughunting technique to mitigate the state explosion problem. In the enumeration process, successor selection is prioritized. We discuss existing guidance and methods to automatically generate them by exploiting system abstractions. We extend the algorithms to feature partialorder reduction and show how liveness problems can be adapted by lifting the search space. For deterministic, finite domains we instantiate the algorithms to directed symbolic, external and distributed search. For realtime domains we discuss the adaption of the algorithms to timed automata and for probabilistic domains we show the application to counterexample generation. Last but not least, we explain how directed model checking helps to accelerate finding solutions to scheduling problems. 1
Domainindependent structured duplicate detection
 In Proc. of the 21st National Conference on Artificial Intelligence (AAAI06
, 2006
"... The scalability of graphsearch algorithms can be greatly extended by using external memory, such as disk, to store generated nodes. We consider structured duplicate detection, an approach to externalmemory graph search that limits the number of slow disk I/O operations needed to access search nod ..."
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Cited by 9 (4 self)
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The scalability of graphsearch algorithms can be greatly extended by using external memory, such as disk, to store generated nodes. We consider structured duplicate detection, an approach to externalmemory graph search that limits the number of slow disk I/O operations needed to access search nodes stored on disk by using an abstract representation of the graph to localize memory references. For graphs with sufficient locality, structured duplicate detection outperforms other approaches to externalmemory graph search. We develop an automatic method for creating an abstract representation that reveals the local structure of a graph. We then integrate this approach into a domainindependent STRIPS planner and show that it dramatically improves scalability for a wide range of planning problems. The success of this approach strongly suggests that similar local structure can be found in many other graphsearch problems.
Sequential and parallel algorithms for frontier A* with delayed duplicate detection
 In Proceedings of the 21st national conference on Artificial intelligence (AAAI06
, 2006
"... We present sequential and parallel algorithms for Frontier A * (FA*) algorithm augmented with a form of Delayed Duplicate Detection (DDD). The sequential algorithm, FA*DDD, overcomes the leakback problem associated with the combination of FA * and DDD. The parallel algorithm, PFA*DDD, is a parall ..."
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Cited by 9 (0 self)
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We present sequential and parallel algorithms for Frontier A * (FA*) algorithm augmented with a form of Delayed Duplicate Detection (DDD). The sequential algorithm, FA*DDD, overcomes the leakback problem associated with the combination of FA * and DDD. The parallel algorithm, PFA*DDD, is a parallel version of FA*DDD that features a novel workload distribution strategy based on intervals. We outline an implementation of PFA*DDD designed to run on a cluster of workstations. The implementation computes intervals at runtime that are tailored to fit the workload at hand. Because the implementation distributes the workload in a manner that is both automated and adaptive, it does not require the user to specify a workload mapping function, and, more importantly, it is applicable to arbitrary problems that may be irregular. We present the results of an experimental evaluation of the implementation where it is used to solve instances of the multiple sequence alignment problem on a cluster of workstations running on top of a commodity network. Results demonstrate that the implementation offers improved capability in addition to improved performance.