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BnBADOPT: An asynchronous branchandbound DCOP algorithm
 In Proceedings of AAMAS
, 2008
"... Abstract. Distributed constraint optimization problems (DCOPs) are a popular way of formulating and solving agentcoordination problems. It is often desirable to solve DCOPs optimally with memorybounded and asynchronous algorithms. We thus introduce BranchandBound ADOPT (BnBADOPT), a memoryboun ..."
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Cited by 64 (21 self)
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Abstract. Distributed constraint optimization problems (DCOPs) are a popular way of formulating and solving agentcoordination problems. It is often desirable to solve DCOPs optimally with memorybounded and asynchronous algorithms. We thus introduce BranchandBound ADOPT (BnBADOPT), a memorybounded asynchronous DCOP algorithm that uses the message passing and communication framework of ADOPT, a well known memorybounded asynchronous DCOP algorithm, but changes the search strategy of ADOPT from bestfirst search to depthfirst branchandbound search. Our experimental results show that BnBADOPT is up to one order of magnitude faster than ADOPT on a variety of large DCOPs and faster than NCBB, a memorybounded synchronous DCOP algorithm, on most of these DCOPs. 1
Multiagent event recognition in structured scenarios
"... We present a framework for the automatic recognition of complex multiagent events in settings where structure is imposed by rules that agents must follow while performing activities. Given semantic spatiotemporal descriptions of what generally happens (i.e., rules, event descriptions, physical con ..."
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Cited by 47 (3 self)
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We present a framework for the automatic recognition of complex multiagent events in settings where structure is imposed by rules that agents must follow while performing activities. Given semantic spatiotemporal descriptions of what generally happens (i.e., rules, event descriptions, physical constraints), and based on video analysis, we determine the events that occurred. Knowledge about spatiotemporal structure is encoded using firstorder logic using an approach based on Allen’s Interval Logic, and robustness to lowlevel observation uncertainty is provided by Markov Logic Networks (MLN). Our main contribution is that we integrate intervalbased temporal reasoning with probabilistic logical inference, relying on an efficient bottomup grounding scheme to avoid combinatorial explosion. Applied to oneonone basketball, our framework detects and tracks players, their hands and feet, and the ball, generates event observations from the resulting trajectories, and performs probabilistic logical inference to determine the most consistent sequence of events. We demonstrate our approach on 1hr (100,000 frames) of outdoor videos. 1.
Distributed problem solving
 AI Magazine
, 2012
"... Broadly, distributed problem solving is a subfield withinmultiagent systems, where the focus is to enable multipleagents to work together to solve a problem. These agents are often assumed to be cooperative, that is, they are part of a team or they are selfinterested but incentives or disincentives ..."
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Cited by 17 (13 self)
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Broadly, distributed problem solving is a subfield withinmultiagent systems, where the focus is to enable multipleagents to work together to solve a problem. These agents are often assumed to be cooperative, that is, they are part of a team or they are selfinterested but incentives or disincentives have been applied such that the individual agent rewards are aligned with the team reward. We illustrate the motivations for distributed problem solving with an example. Imagine a decentralized channelallocation problem in a wireless local area network (WLAN), where each access point (agent) in the WLAN needs to allocate itself a channel to broadcast such that no two access points with overlapping broadcast regions (neighboring agents) are allocated the same channel to avoid interference. Figure 1 shows example mobile WLAN access points, where each access point is a Create robot fitted with a wireless CenGen radio card. Figure 2a shows an illustration of such a problem with three access points in a WLAN, where each oval ring represents the broadcast region of an access point. This problem can, in principle, be solved with a centralized approach by having each and every agent transmit all the relevant information, that is, the set of possible channels that the agent can allocate itself and its set of neighboring agents, to a centralized server. However, this centralized approach may incur unnecessary communication cost compared to a distrib
Memory Intensive AND/OR Search for Combinatorial Optimization in Graphical Models
"... In this paper we explore the impact of caching on search in the context of the recent framework of AND/OR search in graphical models. Specifically, we extend the depthfirst AND/OR BranchandBound tree search algorithm to explore an AND/OR search graph by equipping it with an adaptive caching schem ..."
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Cited by 15 (9 self)
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In this paper we explore the impact of caching on search in the context of the recent framework of AND/OR search in graphical models. Specifically, we extend the depthfirst AND/OR BranchandBound tree search algorithm to explore an AND/OR search graph by equipping it with an adaptive caching scheme similar to good and nogood recording. Furthermore, we present bestfirst search algorithms for traversing the same underlying AND/OR search graph and compare both depthfirst and bestfirst approaches empirically. We focus on two common optimization problems in graphical models: finding the Most Probable Explanation (MPE) in belief networks and solving Weighted CSPs (WCSP). In an extensive empirical evaluation we demonstrate conclusively the superiority of the memory intensive AND/OR search algorithms on a variety of benchmarks including random and realworld problem instances.
Towards Parallel Search for Optimization in Graphical Models
"... We introduce a strategy for parallelizing a stateoftheart sequential search algorithm for optimization on a grid of computers. Based on the AND/OR graph search framework, the procedure exploits the structure of the underlying problem graph. Worker nodes concurrently solve subproblems that are gen ..."
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Cited by 7 (5 self)
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We introduce a strategy for parallelizing a stateoftheart sequential search algorithm for optimization on a grid of computers. Based on the AND/OR graph search framework, the procedure exploits the structure of the underlying problem graph. Worker nodes concurrently solve subproblems that are generated by a single master process. Subproblem generation is itself embedded into an AND/OR Branch and Bound algorithm and dynamically takes previous subproblem solutions into account. Drawing upon the underlying graph structure, we provide some theoretical analysis of the parallelization parameters. A prototype has been implemented and we present promising initial experimental results on genetic haplotyping and Mastermind problem instances, at the same time outlining several open questions. 1
Dr.Fill: Crosswords and an Implemented Solver for Singly Weighted CSPs
"... We describe Dr.Fill, a program that solves Americanstyle crossword puzzles. From a technical perspective, Dr.Fill works by converting crosswords to weighted csps, and then using a variety of novel techniques to find a solution. These techniques include generally applicable heuristics for variable a ..."
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Cited by 4 (0 self)
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We describe Dr.Fill, a program that solves Americanstyle crossword puzzles. From a technical perspective, Dr.Fill works by converting crosswords to weighted csps, and then using a variety of novel techniques to find a solution. These techniques include generally applicable heuristics for variable and value selection, a variant of limited discrepancy search, and postprocessing and partitioning ideas. Branch and bound is not used, as it was incompatible with postprocessing and was determined experimentally to be of little practical value. Dr.Fill’s performance on crosswords from the American Crossword Puzzle Tournament suggests that it ranks among the top fifty or so crossword solvers in the world. 1.
A Case Study in Complexity Estimation: Towards Parallel BranchandBound over Graphical Models
"... We study the problem of complexity estimation in the context of parallelizing an advanced Branch and Boundtype algorithm over graphical models. The algorithm’s pruning power makes load balancing, one crucial element of every distributed system, very challenging. We propose using a statistical regre ..."
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Cited by 4 (3 self)
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We study the problem of complexity estimation in the context of parallelizing an advanced Branch and Boundtype algorithm over graphical models. The algorithm’s pruning power makes load balancing, one crucial element of every distributed system, very challenging. We propose using a statistical regression model to identify and tackle disproportionally complex parallel subproblems, the cause of load imbalance, ahead of time. The proposed model is evaluated and analyzed on various levels and shown to yield robust predictions. We then demonstrate its effectiveness for load balancing in practice. 1
Search Algorithms for M Best Solutions for Graphical Models
"... The paper focuses on finding the m best solutions to combinatorial optimization problems using BestFirst or BranchandBound search. Specifically, we present mA*, extending the wellknown A * to the mbest task, and prove that all its desirable properties, including soundness, completeness and opti ..."
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Cited by 4 (1 self)
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The paper focuses on finding the m best solutions to combinatorial optimization problems using BestFirst or BranchandBound search. Specifically, we present mA*, extending the wellknown A * to the mbest task, and prove that all its desirable properties, including soundness, completeness and optimal efficiency, are maintained. Since BestFirst algorithms have memory problems, we also extend the memoryefficient DepthFirst BranchandBound to the mbest task. We extend both algorithms to optimization tasks over graphical models (e.g., Weighted CSP and MPE in Bayesian networks), provide complexity analysis and an empirical evaluation. Our experiments with 5 variants of BestFirst and BranchandBound confirm that BestFirst is largely superior when memory is available, but BranchandBound is more robust, while both styles of search benefit greatly when the heuristic evaluation function has increased accuracy. 1
Minibucket Elimination with Moment Matching
"... We investigate a hybrid of two styles of algorithms for deriving bounds for optimization tasks over graphical models: noniterative messagepassing schemes exploiting variable duplication to reduce cluster sizes (e.g. MBE) and iterative methods that reparameterize the problem’s functions aiming to ..."
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Cited by 3 (0 self)
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We investigate a hybrid of two styles of algorithms for deriving bounds for optimization tasks over graphical models: noniterative messagepassing schemes exploiting variable duplication to reduce cluster sizes (e.g. MBE) and iterative methods that reparameterize the problem’s functions aiming to produce good bounds even if functions are processed independently (e.g. MPLP). In this work we combine both ideas, augmenting MBE with reparameterization, which we call MBE with Moment Matching (MBEMM). The results of preliminary empirical evaluations show the clear promise of the hybrid scheme over its individual components (e.g., pure MBE and pure MPLP). Most significantly, we demonstrate the potential of the new bounds in improving the power of mechanically generated heuristics for branch and bound search. 1
Predicting the Size of DepthFirst Branch and Bound Search Trees
"... This paper provides algorithms for predicting the size of the Expanded Search Tree (EST) of Depthfirst Branch and Bound algorithms (DFBnB) for optimization tasks. The prediction algorithm is implemented and evaluated in the context of solving combinatorial optimization problems over graphical models ..."
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Cited by 3 (2 self)
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This paper provides algorithms for predicting the size of the Expanded Search Tree (EST) of Depthfirst Branch and Bound algorithms (DFBnB) for optimization tasks. The prediction algorithm is implemented and evaluated in the context of solving combinatorial optimization problems over graphical models such as Bayesian and Markov networks. Our methods extend to DFBnB the approaches provided by KnuthChen schemes that were designed and applied for predicting the EST size of backtracking search algorithms. Our empirical results demonstrate good predictions which are superior to competing schemes. 1