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

Alrayes, Bedour; Kafalı, Özgür; Stathis, Kostas

doi:10.1007/978-3-319-30307-9_10

Cited by 5 publications

(15 citation statements)

References 14 publications

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“…We have also shown how Conan decides when to accept and reqToReserve offers and when not to further pursue a negotiation thread any more. We have implemented our negotiation framework using the Recon platform [2] to evaluate our work, and we have created realistic e-market experiments by varying parameters such as the market population and the demand/supply ratio. Moreover, we have relaxed most of the assumptions made in previous work, in particular how to deal with private deadlines and the idea of an open market where buyers and sellers may come and go at any time.…”

Section: Discussionmentioning

confidence: 99%

“…storage price). Also, the GOLEM agent platform [12] used to build our negotiation testbed RECON [2], has facilities that can include humans as avatars in the experiments (see [14] for an example of user immersed in an e-retailing agent environment), so our approach is generalisable for negotiations between humans and agents [41]. However, a detailed discussion of these issues is beyond the scope of this paper but covered by our plan for future work.…”

Section: Discussionmentioning

confidence: 99%

“…We build a negotiation framework using Recon [2], an experimental simulation platform that supports the development of software agents interacting concurrently with other agents in negotiation domains. Unlike existing simulation toolkits [42], Recon also supports declarative strategies to enable agents explain their decisions to a user, if necessary.…”

Section: Methodsmentioning

confidence: 99%

“…Given the score combinations of Opponent's response time and Opponent's concession rate, the domain of σ i is [2,6], with 2 implying that the opponent is compatible with our style of negotiation and 6 implying that the opponent is incompatible. We then derive the global negotiation situation σ for buyer b, which we calculate as the sum of all the local negotiation situations σ i , as shown in Eq.…”

Section: Self Factormentioning

confidence: 99%

“…We proceed as follows. We outline first our negotiation simulation framework Recon [2], which implements the negotiation agents and facilitates the way we have generated our experimental results. We present two large experiments to test Conan.…”

Section: Empirical Evaluationmentioning

confidence: 99%

See 4 more Smart Citations

Concurrent bilateral negotiation for open e-markets: the Conan strategy

2017

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We develop a novel strategy that supports software agents to make decisions on how to negotiate for a resource in open and dynamic e-markets. Although existing negotiation strategies offer a number of sophisticated features, including modelling an opponent and negotiating with many opponents simultaneously, they abstract away from the dynamicity of the market and the model that the agent holds for itself in terms of ongoing negotiations, thus ignoring information that increases an agent's utility. Our proposed strategy COncurrent Negotiating AgeNts (Conan) considers a weighted combination of modelling the market environment and the progress of concurrent negotiations in which the agent partakes. We conduct extensive experiments to evaluate the strategy's performance in various settings where different opponents from the literature provide a competitive market. Our experiments provide statistically significant results showing how Conan outperforms the state-of-the-art in terms of the utility gained during negotiations.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Self Factormentioning

confidence: 99%

Section: Empirical Evaluationmentioning

confidence: 99%

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Concurrent bilateral negotiation for open e-markets: the Conan strategy

2017

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ANEGMA: an automated negotiation model for e-markets

Bagga

Paoletti

Alrayes

et al. 2021

Auton Agent Multi-Agent Syst

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We present a novel negotiation model that allows an agent to learn how to negotiate during concurrent bilateral negotiations in unknown and dynamic e-markets. The agent uses an actor-critic architecture with model-free reinforcement learning to learn a strategy expressed as a deep neural network. We pre-train the strategy by supervision from synthetic market data, thereby decreasing the exploration time required for learning during negotiation. As a result, we can build automated agents for concurrent negotiations that can adapt to different e-market settings without the need to be pre-programmed. Our experimental evaluation shows that our deep reinforcement learning based agents outperform two existing well-known negotiation strategies in one-to-many concurrent bilateral negotiations for a range of e-market settings.

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Pirasa: strategic protocol selection for e-commerce agents

et al. 2018

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We present PIRASA: an agent-based simulation environment for studying how autonomous agents can best interact with each other to exchange goods in e-commerce marketplaces. A marketplace in PIRASA enables agents to enact buyer or seller roles and select from sales, auction, and negotiation protocols to achieve the individual goals of their users. An agent's strategy to maximize its utility in the marketplace is guided by its user's preferences and constraints such as 'maximum price' and 'deadline', as well as an agent's personality attributes, e.g., how 'eager' or 'late' the agent can be for exchanging goods and whether the agent is a 'spender' or 'saver' in an exchange. To guide the agent's actions selected by a strategy, we use the notion of electronic contracts formulated as regulatory norms. In this context, we present how PIRASA is organized with regards to seller processes for goods submission, the inclusion of buyer preferences, and the management of transactions through specialized broker agents. Using randomized simulations, we demonstrate how a buyer agent can strategically select the most suitable protocol to satisfy its user's preferences, goals and constraints in dynamically changing market settings. The generated simulation data can be leveraged by researchers to analyze agent behaviors, and develop additional strategies.

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

Cited by 5 publications

References 14 publications

Concurrent bilateral negotiation for open e-markets: the Conan strategy

Concurrent bilateral negotiation for open e-markets: the Conan strategy

ANEGMA: an automated negotiation model for e-markets

Pirasa: strategic protocol selection for e-commerce agents

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