Titlebook: KI 2016: Advances in Artificial Intelligence; 39th Annual German C Gerhard Friedrich,Malte Helmert,Franz Wotawa Conference proceedings 2016

STEER

Providing Built-In Counters in a Declarative Dynamic Programming Environmentip D-FLAT with built-in counters in order to shift certain computations from the ASP side to the internal part of D-FLAT. In this paper, we highlight this new feature and provide empirical benchmarks on weighted versions of the . problem showing that our new version increases D-FLAT’s robustness and efficiency.

檔案

A Fast Elimination Method for Pruning in POMDPsrithm for detecting these useless vectors. We claim that using the information about the support region of the vectors that have already been processed, both algorithms can be drastically improved. We present comparative experiments on both randomly generated problems and POMDP benchmarks.

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彎彎曲曲

Declarative Decomposition and Dispatching for Large-Scale Job-Shop Schedulingarative programming for high adaptability and maintainability. We present first results proving the concept of our novel scheduling approach based on a new large-scale job-shop benchmark with proven optimal solutions.

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esculent

Providing Built-In Counters in a Declarative Dynamic Programming Environmentce. The dynamic programming algorithm is specified by means of Answer-Set Programming (ASP), allowing for declarative and succinct specifications. D-FLAT traverses the tree decomposition and calls an ASP system with the provided specification at each tree decomposition node. It is thus crucial that

BUOY

Admire

Lifted Junction Tree Algorithm variable elimination. To benefit from the advantages of the junction tree algorithm for inference in the first-order setting, we transfer the idea of lifting to the junction tree algorithm..Our lifted junction tree algorithm aims at reducing computations by introducing first-order junction trees th

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里程碑

A Fast Elimination Method for Pruning in POMDPsd by a finite set of vectors over the state space. In each step of the exact value iteration algorithm, the number of possible vectors increases linearly with the cardinality of the action set and exponentially with the cardinality of the observation set. This set of vectors should be pruned to a mi