The Learning Shell: Automated Macro Construction

Jacobs, Nico; Blockeel, Hendrik

doi:10.1007/3-540-44566-8_4

Cited by 6 publications

(4 citation statements)

References 4 publications

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“…Korvemaker and Greiner [12] have developed this idea into a system which predicts whole command lines. The Unix command prediction problem has also been addressed by Jacobs and Blockeel who infer macros from frequent command sequences [8] and predict the next command using variable memory Markov models [9,10].…”

Section: Related Workmentioning

confidence: 99%

Sentence completion

Grabski

Scheffer

2004

Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

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We discuss a retrieval model in which the task is to complete a sentence, given an initial fragment, and given an application specific document collection. This model is motivated by administrative and call center environments, in which users have to write documents with a certain repetitiveness. We formulate the problem setting and discuss appropriate performance metrics. We present an index-based retrieval algorithm and a cluster-based approach, and evaluate our algorithms using collections of emails that have been written by two distinct service centers.

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Section: Related Workmentioning

confidence: 99%

Sentence completion

Grabski

Scheffer

2004

Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval

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“….Thus, commands are essentially structured. Tasks that have been considered for UNIX command sequences include the prediction of the next command in the sequence (Davison & Hirsh, 1998), the classification of a command sequence in a user category (Korvemaker & Greiner, 2000;Jacobs & Blockeel, 2001), and anomaly detection (Lane, 1999). Traditional HMMs cannot easily deal with this type of structured sequences.…”

Section: Appendix C An Example Of a Complex Interaction Protocolmentioning

confidence: 99%

Representing Conversations for Scalable Overhearing

Gutnik¹,

Kaminka²

2006

jair

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Open distributed multi-agent systems are gaining interest in the academic community and in industry. In such open settings, agents are often coordinated using standardized agent conversation protocols. The representation of such protocols (for analysis, validation, monitoring, etc) is an important aspect of multi-agent applications. Recently, Petri nets have been shown to be an interesting approach to such representation, and radically different approaches using Petri nets have been proposed. However, their relative strengths and weaknesses have not been examined. Moreover, their scalability and suitability for different tasks have not been addressed. This paper addresses both these challenges. First, we analyze existing Petri net representations in terms of their scalability and appropriateness for overhearing, an important task in monitoring open multi-agent systems. Then, building on the insights gained, we introduce a novel representation using Colored Petri nets that explicitly represent legal joint conversation states and messages. This representation approach offers significant improvements in scalability and is particularly suitable for overhearing. Furthermore, we show that this new representation offers a comprehensive coverage of all conversation features of FIPA conversation standards. We also present a procedure for transforming AUML conversation protocol diagrams (a standard humanreadable representation), to our Colored Petri net representation.

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“…Korvemaker and Greiner (2000) have developed this idea into a system which predicts entire command lines. The Unix command prediction problem has also been addressed by Jacobs and Blockeel (2001) who infer macros from frequent command sequences and predict the next command using variable memory Markov models (Jacobs and Blockeel, 2003).…”

Section: Introductionmentioning

confidence: 99%

Predicting sentences using N-gram language models

Bickel

Haider

Scheffer

2005

Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05

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We explore the benefit that users in several application areas can experience from a "tab-complete" editing assistance function. We develop an evaluation metric and adapt N-gram language models to the problem of predicting the subsequent words, given an initial text fragment. Using an instance-based method as baseline, we empirically study the predictability of call-center emails, personal emails, weather reports, and cooking recipes.

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The Learning Shell: Automated Macro Construction

Cited by 6 publications

References 4 publications

Sentence completion

Sentence completion

Representing Conversations for Scalable Overhearing

Predicting sentences using N-gram language models

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