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RECON: A Robust Multi-agent Environment for Simulating COncurrent Negotiations

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Recent Advances in Agent-based Complex Automated Negotiation

Part of the book series: Studies in Computational Intelligence ((SCI,volume 638))

Abstract

Recon is an experimental simulation platform that supports the development of software agents interacting concurrently with other agents in negotiation domains. Unlike existing simulation toolkits that support only imperative negotiation strategies, Recon also supports declarative strategies, for applications where logic-based agents need to explain their negotiation decisions to a user. Recon is built on top of the GOLEM agent platform, specialized with a set of infrastructure agents that can manage an electronic market and extract statistics from the negotiations that take place. We evaluate the performance of Recon by showing how by increasing the number of agents in a simulation affects the agents’ time to make an offer during negotiation.

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Notes

  1. 1.

    http://golem.cs.rhul.ac.uk/.

  2. 2.

    Listing 4 in the Appendix illustrates an example of a log file.

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Acknowledgments

We thank Ataul Munim for supporting us with developing the simulation environment. The first author wishes to acknowledge the support of a scholarship from the King Saud University.

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Authors and Affiliations

  1. Department of Computer Science, Royal Holloway, University of London, Egham Hill, Egham, TW20 0EX, UK

    Bedour Alrayes, Özgür Kafalı & Kostas Stathis

Authors
  1. Bedour Alrayes
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  2. Özgür Kafalı
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  3. Kostas Stathis
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Corresponding author

Correspondence to Bedour Alrayes .

Editor information

Editors and Affiliations

  1. Hamamatsu, Shizuoka, Japan

    Naoki Fukuta

  2. Nagoya Institute of Technology, Nagoya, Japan

    Takayuki Ito

  3. School of Computer Science, University of Wollongong, Wollongong, New South Wales, Australia

    Minjie Zhang

  4. Tokyo University of Agriculture and, Tokyo, Japan

    Katsuhide Fujita

  5. Institute of Sensors, Signals, Heriot-Watt University, Riccarton, United Kingdom

    Valentin Robu

Appendix: Agents in RECON

Appendix: Agents in RECON

This section describes briefly the basic methods used by the market agents. Listings 2 and 3 presents examples of a Java buyer agent and a Prolog buyer agent, respectively. Note that the offer generation processes of the agents solely depend on their strategies and not on Recon. Seller agents can be implemented similarly.

figure b

In Listing 2, the Java buyer inherits the basic methods from AbstractBuyerAgent (line 1). The method decideActionBasedOnOffer() (line 7) returns an action based on the seller’s offer. The method getUtility() (line 9) returns the utility of the seller offer. The method generateNextOffer() (line 10) calls the method in line 39 to generate the buyer next offer. This is part of the buyer strategy. The method isOfferAcceptable() (line 12) calls the method in line 43 to decide if the opponent’s offer is acceptable by comparing the buyer offer with the opponent offer. This is part of the buyer strategy. The method decideActionBasedOnAccept() (line 26) returns an action based on the opponent accept action.

In Listing 3, select(_, exit_all(Item), T) (line 1) returns the action exit_all for all sellers that negotiate for item Item at time T. This decision will be taken if the buyer reaches its deadline. The predicate select(Goal, offer(Opponent, Item, Offer),T) (line 7) returns an offer (represented by the variable Offer) for the agent’s goal (Goal), seller (Opponent) and item under negotiation (Item). This predicate calls a function to generate the counter offer at line 12 after checking the buyer’s deadline. The predicate calc_next_offer(buy(Id, Ao), Offer, T) (line 15) generates the counteroffer at time T for negotiation dialogue (Id) based on: initial price (Min), reservation price (Max) and concession rate (CA). The predicate concession(buy(Id, Ao), CA, T) (line 22) calculates the concession rate. This part of the agent strategy.

figure c
figure d

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Alrayes, B., Kafalı, Ö., Stathis, K. (2016). RECON: A Robust Multi-agent Environment for Simulating COncurrent Negotiations. In: Fukuta, N., Ito, T., Zhang, M., Fujita, K., Robu, V. (eds) Recent Advances in Agent-based Complex Automated Negotiation. Studies in Computational Intelligence, vol 638. Springer, Cham. https://doi.org/10.1007/978-3-319-30307-9_10

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  • DOI: https://doi.org/10.1007/978-3-319-30307-9_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30305-5

  • Online ISBN: 978-3-319-30307-9

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