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First Steps in Chat-Based Negotiating Agents

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Next Frontier in Agent-Based Complex Automated Negotiation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 596))

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Abstract

To date, a variety of automated negotiation agents have been created. While each of these agents has been shown to be effective in negotiating with people in specific environments, they lack natural language processing (NLP) methods required to enable real-world types of interactions. In this paper we study how existing agents must be modified to address this limitation. After performing an extensive study of agents’ negotiation with human subjects, we found that simply modifying existing agents to include an NLP module is insufficient to create these agents. Instead the agents’ strategies must be modified to address offers that do not include values for all the discussed issues (as is the case in menu-based interfaces) and consequently issue-by-issue interactions.

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Notes

  1. 1.

    The exact functions (and the complete domain) are available in the GENIUS framework that can be freely downloaded from the Internet.

  2. 2.

    www.json.org.

  3. 3.

    The state-of-the-art in NLU for dialog systems is sequence classification [12]. We decided against this option because it requires too much labeling effort: while in multi-label classification you only need to label each sentence, in sequence classification you must label each fraction of a sentence. After deciding to use multilabel classifiers, we checked various approaches to multi-label classification [21] and various kinds of base binary classifiers. We found out that the combination of HOMER with Modified Balanced Winnow, described above, had the best performance in terms of both classification accuracy and run-time.

  4. 4.

    We tried more sophisticated features, such as pairs of non-adjacent words, but this didn’t improve performance.

  5. 5.

    Sentence-level accuracy is the number of sentences whose classification was exactly correct (i.e., the set of dialog acts returned by the MLC is identical to the correct set), divided by the total number of sentences. The 72 % accuracy was calculated using 5-fold cross-validation on the set of 775 tagged sentences. Sentence-level accuracy is the strictest possible performance measure. In other measures, such as precision, recall or F1, the performance of our NLU was higher

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Author information

Authors and Affiliations

  1. Department of Industrial Engineering and Management, Ariel University, 4070000, Ariel, Israel

    Inon Zuckerman

  2. Department of Computer Science, Bar-Ilan University, 5290002, Ramat-gan, Israel

    Erel Segal-Halevi & Sarit Kraus

  3. Department of Industrial Engineering, Jerusalem College of Technology, 9116001, Jerusalem, Israel

    Avi Rosenfeld

Authors
  1. Inon Zuckerman
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  2. Erel Segal-Halevi
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  3. Avi Rosenfeld
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  4. Sarit Kraus
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Corresponding author

Correspondence to Inon Zuckerman .

Editor information

Editors and Affiliations

  1. Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology, Tokyo, Japan

    Katsuhide Fujita

  2. School of Techno-Business Administration, Nagoya Institute of Technology, Nagoya, Japan

    Takayuki Ito

  3. School of Computer Science and Software Engineering, The University of Wollongong, Wollongong, NSW, Australia

    Minjie Zhang

  4. School of Electronics and Computer Science, University of Southampton, Southampton, UK

    Valentin Robu

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Zuckerman, I., Segal-Halevi, E., Rosenfeld, A., Kraus, S. (2015). First Steps in Chat-Based Negotiating Agents. In: Fujita, K., Ito, T., Zhang, M., Robu, V. (eds) Next Frontier in Agent-Based Complex Automated Negotiation. Studies in Computational Intelligence, vol 596. Springer, Tokyo. https://doi.org/10.1007/978-4-431-55525-4_6

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  • DOI: https://doi.org/10.1007/978-4-431-55525-4_6

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-55524-7

  • Online ISBN: 978-4-431-55525-4

  • eBook Packages: EngineeringEngineering (R0)

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