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
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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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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.
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.
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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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