The Computational Complexity of Probabilistic Planning

Littman, Michael L.; Goldsmith, Judy; Mundhenk, Martin

doi:10.1613/jair.505

Cited by 130 publications

(104 citation statements)

References 30 publications

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“…Results are shown in Tables 1, 2, and 3. The table 1 reveals slight over-fitting in our models since it did not generalize well when compared to the baselines, i.e., we seem to obtain better accuracy on the training data than the authors in [26] while their system generalize better on unseen data. However, observing deeply, we see only slight variations between train and test accuracy.…”

Section: Discussionmentioning

confidence: 88%

“…Tables 1, 2 and 3 display the results obtained on the datasets used. For table 1, we used the results from Rocktaschel et al, [26], Baudis et al, [2] and Bowman et al, [9] as the baseline systems on SNLI and SICK respectively. For PASCAL-RTE3, we did not include any baseline system since there is no recent work which use similar deep learning approach on that dataset.…”

Section: Bnaic 2016mentioning

confidence: 99%

“…In particular, the authors in [26] using attention-based LSTM have reported state of the art results in textual entailment.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [26] proposed a word-by-word neural attention mechanism based on LSTM. The idea is to have two LSTMs, one reasoning over the sequence of tokens in the text while the other is reasoning on the hypothesis sequence.…”

Section: Introductionmentioning

confidence: 99%

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Functional Representation of Prototypes in LVQ and Relevance Learning

Melchert¹,

Seiffert

Biehl³

2016

Advances in Self-Organizing Maps and Learning Vector Quantization

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Held yearly, the objective of BNAIC is to promote and disseminate recent research developments in Artificial Intelligence within Belgium, Luxembourg and the Netherlands. However, it does not exclude contributions from countries outside the Benelux. As in previous years, BNAIC 2016 welcomed four types of contributions, namely A) regular papers, B) compressed contributions, C) demonstration abstracts, and D) thesis abstracts.We received 93 submissions, consisting of 24 regular papers, 47 short papers, 11 demonstration abstracts and 11 thesis abstracts. After a thorough review phase by the Program Committee, the conference chairs made the final acceptance decisions. The overall acceptance rate was 88% (63% for regular papers, 100% for compressed contributions and demonstration abstracts, and 91% for thesis abstracts).In addition to the regular research presentations, posters and demonstrations, we were happy to include several other elements in the program of BNAIC 2016, among To conclude, we want to express our gratitude to all people who made this conference possible: in addition to all invited speakers mentioned above, many thanks to all organizing and program committee members for their hard work in assuring the high quality of this conference. Moreover, we wish to thank all student volunteers, administrative and secretarial assistants, and of course our sponsors. We also gratefully acknowledge help from the BNVKI and from previous organizers. And last, but certainly not least, we cordially thank all the authors who made important contributions to the conference. Without their efforts, this conference could not have taken place. Inference that aims at determining whether a hypothesis is entailed by a text. Usually tackled by machine learning techniques employing features which represent similarity between texts, the recent availability of more training data presupposes that Neural Networks that are able to learn latent feature from data for generalized prediction could be employed. This paper employs the Child-Sum Tree-LSTM for solving the challenging problem of textual entailment. Our approach is simple and able to generalize well without excessive parameter optimization. Evaluation done on SNLI, SICK and other TE datasets shows the competitiveness of our approach.

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Section: Discussionmentioning

confidence: 88%

Section: Bnaic 2016mentioning

confidence: 99%

“…In particular, the authors in [26] using attention-based LSTM have reported state of the art results in textual entailment.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Functional Representation of Prototypes in LVQ and Relevance Learning

Melchert¹,

Seiffert

Biehl³

2016

Advances in Self-Organizing Maps and Learning Vector Quantization

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“…These approaches try to generate conditional plans and policies (mappings from states to actions), respectively. Unfortunately, both approaches are of much higher complexity [7,8] than classical planning and usually fail to scale in even moderately complex scenarios. Furthermore, it might be impossible to model all potential outcomes of actions in dynamic environments, or concrete probabilities of outcomes are unknown.…”

Section: Planning In Real-world Environmentsmentioning

confidence: 99%

Task Planning for an Autonomous Service Robot

Keller¹,

Eyerich²,

Nebel³

2010

KI 2010: Advances in Artificial Intelligence

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Abstract. In the DESIRE project an autonomous robot capable of performing service tasks in a typical kitchen environment has been developed. The overall system consists of various loosely coupled subcomponents providing particular features like manipulating objects or recognizing and interacting with humans. To bring all these subcomponents together to act as monolithic system, a high-performance planning system has been implemented. In this paper, we present this system's basic architecture and some advanced extensions necessary to cope with the various challenges arising in dynamic and uncertain environments like those a real world service robot is usually faced with.

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