The Experts below are selected from a list of 144234 Experts worldwide ranked by ideXlab platform
Tomas Lozanoperez - One of the best experts on this subject based on the ideXlab platform.
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learning to guide task and motion planning using score space representation
arXiv: Robotics, 2018Co-Authors: Beomjoon Kim, Zi Wang, Leslie Pack Kaelbling, Tomas LozanoperezAbstract:In this paper, we propose a learning algorithm that speeds up the search in task and motion planning Problems. Our algorithm proposes solutions to three different challenges that arise in learning to improve planning efficiency: what to predict, how to represent a planning Problem Instance, and how to transfer knowledge from one Problem Instance to another. We propose a method that predicts constraints on the search space based on a generic representation of a planning Problem Instance, called score-space, where we represent a Problem Instance in terms of the performance of a set of solutions attempted so far. Using this representation, we transfer knowledge, in the form of constraints, from previous Problems based on the similarity in score space. We design a sequential algorithm that efficiently predicts these constraints, and evaluate it in three different challenging task and motion planning Problems. Results indicate that our approach performs orders of magnitudes faster than an unguided planner
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learning to guide task and motion planning using score space representation
International Conference on Robotics and Automation, 2017Co-Authors: Beomjoon Kim, Leslie Pack Kaelbling, Tomas LozanoperezAbstract:In this paper, we propose a learning algorithm that speeds up the search in task and motion planning Problems. Our algorithm proposes solutions to three different challenges that arise in learning to improve planning efficiency: what to predict, how to represent a planning Problem Instance, and how to transfer knowledge from one Problem Instance to another. We propose a method that predicts constraints on the search space based on a generic representation of a planning Problem Instance, called score space, where we represent a Problem Instance in terms of performance of a set of solutions attempted so far. Using this representation, we transfer knowledge, in the form of constraints, from previous Problems based on the similarity in score space. We design a sequential algorithm that efficiently predicts these constraints, and evaluate it in three different challenging task and motion planning Problems. Results indicate that our approach perform orders of magnitudes faster than an unguided planner.
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decidability of semi holonomic prehensile task and motion planning
International Workshop Algorithmic Foundations Robotics, 2016Co-Authors: Ashwin Deshpande, Leslie Pack Kaelbling, Tomas LozanoperezAbstract:In this paper, we define semi-holonomic controllability (SHC) and a general task and motion planning framework. We give a perturbation algorithm that can take a prehensile task and motion planning (PTAMP) domain and create a jointly-controllable-open (JC-open) variant with practically identical semantics. We then present a decomposition-based algorithm that computes the reachability set of a Problem Instance if a controllability criterion is met. Finally, by showing that JC-open domains satisfy the controllability criterion, we can conclude that PTAMP is decidable.
Beomjoon Kim - One of the best experts on this subject based on the ideXlab platform.
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learning to guide task and motion planning using score space representation
arXiv: Robotics, 2018Co-Authors: Beomjoon Kim, Zi Wang, Leslie Pack Kaelbling, Tomas LozanoperezAbstract:In this paper, we propose a learning algorithm that speeds up the search in task and motion planning Problems. Our algorithm proposes solutions to three different challenges that arise in learning to improve planning efficiency: what to predict, how to represent a planning Problem Instance, and how to transfer knowledge from one Problem Instance to another. We propose a method that predicts constraints on the search space based on a generic representation of a planning Problem Instance, called score-space, where we represent a Problem Instance in terms of the performance of a set of solutions attempted so far. Using this representation, we transfer knowledge, in the form of constraints, from previous Problems based on the similarity in score space. We design a sequential algorithm that efficiently predicts these constraints, and evaluate it in three different challenging task and motion planning Problems. Results indicate that our approach performs orders of magnitudes faster than an unguided planner
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learning to guide task and motion planning using score space representation
International Conference on Robotics and Automation, 2017Co-Authors: Beomjoon Kim, Leslie Pack Kaelbling, Tomas LozanoperezAbstract:In this paper, we propose a learning algorithm that speeds up the search in task and motion planning Problems. Our algorithm proposes solutions to three different challenges that arise in learning to improve planning efficiency: what to predict, how to represent a planning Problem Instance, and how to transfer knowledge from one Problem Instance to another. We propose a method that predicts constraints on the search space based on a generic representation of a planning Problem Instance, called score space, where we represent a Problem Instance in terms of performance of a set of solutions attempted so far. Using this representation, we transfer knowledge, in the form of constraints, from previous Problems based on the similarity in score space. We design a sequential algorithm that efficiently predicts these constraints, and evaluate it in three different challenging task and motion planning Problems. Results indicate that our approach perform orders of magnitudes faster than an unguided planner.
Leslie Pack Kaelbling - One of the best experts on this subject based on the ideXlab platform.
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learning to guide task and motion planning using score space representation
arXiv: Robotics, 2018Co-Authors: Beomjoon Kim, Zi Wang, Leslie Pack Kaelbling, Tomas LozanoperezAbstract:In this paper, we propose a learning algorithm that speeds up the search in task and motion planning Problems. Our algorithm proposes solutions to three different challenges that arise in learning to improve planning efficiency: what to predict, how to represent a planning Problem Instance, and how to transfer knowledge from one Problem Instance to another. We propose a method that predicts constraints on the search space based on a generic representation of a planning Problem Instance, called score-space, where we represent a Problem Instance in terms of the performance of a set of solutions attempted so far. Using this representation, we transfer knowledge, in the form of constraints, from previous Problems based on the similarity in score space. We design a sequential algorithm that efficiently predicts these constraints, and evaluate it in three different challenging task and motion planning Problems. Results indicate that our approach performs orders of magnitudes faster than an unguided planner
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learning to guide task and motion planning using score space representation
International Conference on Robotics and Automation, 2017Co-Authors: Beomjoon Kim, Leslie Pack Kaelbling, Tomas LozanoperezAbstract:In this paper, we propose a learning algorithm that speeds up the search in task and motion planning Problems. Our algorithm proposes solutions to three different challenges that arise in learning to improve planning efficiency: what to predict, how to represent a planning Problem Instance, and how to transfer knowledge from one Problem Instance to another. We propose a method that predicts constraints on the search space based on a generic representation of a planning Problem Instance, called score space, where we represent a Problem Instance in terms of performance of a set of solutions attempted so far. Using this representation, we transfer knowledge, in the form of constraints, from previous Problems based on the similarity in score space. We design a sequential algorithm that efficiently predicts these constraints, and evaluate it in three different challenging task and motion planning Problems. Results indicate that our approach perform orders of magnitudes faster than an unguided planner.
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decidability of semi holonomic prehensile task and motion planning
International Workshop Algorithmic Foundations Robotics, 2016Co-Authors: Ashwin Deshpande, Leslie Pack Kaelbling, Tomas LozanoperezAbstract:In this paper, we define semi-holonomic controllability (SHC) and a general task and motion planning framework. We give a perturbation algorithm that can take a prehensile task and motion planning (PTAMP) domain and create a jointly-controllable-open (JC-open) variant with practically identical semantics. We then present a decomposition-based algorithm that computes the reachability set of a Problem Instance if a controllability criterion is met. Finally, by showing that JC-open domains satisfy the controllability criterion, we can conclude that PTAMP is decidable.
Frank Neumann - One of the best experts on this subject based on the ideXlab platform.
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Feature-Based Diversity Optimization for Problem Instance Classification
Evolutionary computation, 2020Co-Authors: Wanru Gao, Samadhi Nallaperuma, Frank NeumannAbstract:Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Traveling Salesperson Problem. In this paper, we present a general framework that is able to construct a diverse set of Instances that are hard or easy for a given search heuristic. Such a diverse set is obtained by using an evolutionary algorithm for constructing hard or easy Instances that are diverse with respect to different features of the underlying Problem. Examining the constructed Instance sets, we show that many combinations of two or three features give a good classification of the TSP Instances in terms of whether they are hard to be solved by 2-OPT.
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Automated Algorithm Selection: Survey and Perspectives
Evolutionary Computation, 2019Co-Authors: Pascal Kerschke, Frank Neumann, Holger H. Hoos, Heike TrautmannAbstract:It has long been observed that for practically any computational Problem that has been intensely studied, different Instances are best solved using different algorithms. This is particularly pronounced for computationally hard Problems, where in most cases, no single algorithm defines the state of the art; instead, there is a set of algorithms with complementary strengths. This performance complementarity can be exploited in various ways, one of which is based on the idea of selecting, from a set of given algorithms, for each Problem Instance to be solved the one expected to perform best. The task of automatically selecting an algorithm from a given set is known as the per-Instance algorithm selection Problem and has been intensely studied over the past 15 years, leading to major improvements in the state of the art in solving a growing number of discrete combinatorial Problems, including propositional satisfiability and AI planning. Per-Instance algorithm selection also shows much promise for boosting performance in solving continuous and mixed discrete/continuous optimisation Problems. This survey provides an overview of research in automated algorithm selection, ranging from early and seminal works to recent and promising application areas. Different from earlier work, it covers applications to discrete and continuous Problems, and discusses algorithm selection in context with conceptually related approaches, such as algorithm configuration, scheduling, or portfolio selection. Since informative and cheaply computable Problem Instance features provide the basis for effective per-Instance algorithm selection systems, we also provide an overview of such features for discrete and continuous Problems. Finally, we provide perspectives on future work in the area and discuss a number of open research challenges.
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PPSN - Feature-Based Diversity Optimization for Problem Instance Classification
Parallel Problem Solving from Nature – PPSN XIV, 2016Co-Authors: Wanru Gao, Samadhi Nallaperuma, Frank NeumannAbstract:Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Traveling Salesperson Problem. In this paper, we present a general framework that is able to construct a diverse set of Instances that are hard or easy for a given search heuristic. Such a diverse set is obtained by using an evolutionary algorithm for constructing hard or easy Instances that are diverse with respect to different features of the underlying Problem. Examining the constructed Instance sets, we show that many combinations of two or three features give a good classification of the TSP Instances in terms of whether they are hard to be solved by 2-OPT.
Jacob Biamonte - One of the best experts on this subject based on the ideXlab platform.
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Non−perturbative k−body to two−body commuting conversion Hamiltonians and embedding Problem Instances into Ising spins
Physical Review A, 2008Co-Authors: Jacob BiamonteAbstract:An algebraic method has been developed which allows one to engineer several energy levels including the low-energy subspace of interacting spin systems. By introducing ancillary qubits, this approach allows k-body interactions to be captured exactly using 2-body Hamiltonians. Our method works when all terms in the Hamiltonian share the same basis and has no dependence on perturbation theory or the associated large spectral gap. Our methods allow Problem Instance solutions to be embedded into the ground energy state of Ising spin systems. Adiabatic evolution might then be used to place a computational system into it's ground state.
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nonperturbative k body to two body commuting conversion hamiltonians and embedding Problem Instances into ising spins
Physical Review A, 2008Co-Authors: Jacob BiamonteAbstract:An algebraic method has been developed which allows one to engineer several energy levels including the low-energy subspace of interacting spin systems. By introducing ancillary qubits, this approach allows k-body interactions to be captured exactly using 2-body Hamiltonians. Our method works when all terms in the Hamiltonian share the same basis and has no dependence on perturbation theory or the associated large spectral gap. Our methods allow Problem Instance solutions to be embedded into the ground energy state of Ising spin systems. Adiabatic evolution might then be used to place a computational system into it's ground state.
