Computers That Negotiate on Our Behalf: Major Challenges for Self-sufficient, Self-directed, and Interdependent Negotiating Agents

Baarslag, Tim; Kaisers, Michael; Gerding, Enrico H.; Jonker, Catholijn M.; Gratch, Jonathan

doi:10.1007/978-3-319-71679-4_10

Cited by 13 publications

(11 citation statements)

References 60 publications

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“…The resulting exchanges do not excessively congest the physical network in terms of peak power compared to that of Individual control. and may need to elicit them from prosumers in a cost-effective way [33,34]. Future work may investigate the case where the agents exhibit uncertainty over the preferences and are required to negotiate successfully with partial preferences.…”

Section: Resultsmentioning

confidence: 99%

Automated peer-to-peer negotiation for energy contract settlements in residential cooperatives

2020

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This paper presents an automated peer-to-peer negotiation strategy for settling energy contracts among prosumers in a Residential Energy Cooperative considering heterogeneity prosumer preferences. The heterogeneity arises from prosumers' evaluation of energy contracts through multiple societal and environmental criteria and the prosumers' private preferences over those criteria. The prosumers engage in bilateral negotiations with peers to mutually agree on periodical energy contracts/loans consisting of the energy volume to be exchanged at that period and the return time of the exchanged energy. The negotiating prosumers navigate through a common negotiation domain consisting of potential energy contracts and evaluate those contracts from their valuations on the entailed criteria against a utility function that is robust against generation and demand uncertainty. From the repeated interactions, a prosumer gradually learns about the compatibility of its peers in reaching energy contracts that are closer to Nash solutions. Empirical evaluation on real demand, generation and storage profiles -in multiple system scales -illustrates that the proposed negotiation based strategy can increase the system efficiency (measured by utilitarian social welfare) and fairness (measured by Nash social welfare) over a baseline strategy and an individual flexibility control strategy representing the status quo strategy. We thus elicit system benefits from peer-to-peer flexibility exchange already without any central coordination and market operator, providing a simple yet flexible and effective paradigm that complements existing markets.

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Section: Resultsmentioning

confidence: 99%

Automated peer-to-peer negotiation for energy contract settlements in residential cooperatives

2020

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“…There are still many open challenges for automated negotiation [6,7], such as strategy learning, opponent recognition, domain learning, preference elicitation and reasoning. The Automated Negotiation league in 2019, informally known as the GENIUS league, focused on negotiating agents that receive partial preference information.…”

Section: Automated Negotiation Main Leaguementioning

confidence: 99%

Challenges and Main Results of the Automated Negotiating Agents Competition (ANAC) 2019

Aydoğan

Baarslag

Fujita

et al. 2020

Multi-Agent Systems and Agreement Technologies

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The Automated Negotiating Agents Competition (ANAC) is a yearly-organized international contest in which participants from all over the world develop intelligent negotiating agents for a variety of negotiation problems. To facilitate the research on agent-based negotiation, the organizers introduce new research challenges every year. ANAC 2019 posed five negotiation challenges: automated negotiation with partial preferences, repeated human-agent negotiation, negotiation in supplychain management, negotiating in the strategic game of Diplomacy, and in the Werewolf game. This paper introduces the challenges and discusses the main findings and lessons learnt per league.

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“…Well-known bidding strategies include the time-dependent bidding tactics where the result of the decision functions is based on the time passed in the negotiation [9,13] as follows:…”

Section: Negotiation Protocolmentioning

confidence: 99%

“…where P min , P max are the minimum and maximum accepted offers, t is the normalized 3 time t ∈ [0, 1] and k ∈ [0, 1] is the utility of the first offer. If 0 < e < 1 the agent does not reduce its target utility in the early stages of the negotiation and concedes at the end of the deadline [9,13]. The agent that follows this type of strategy is called Boulware.…”

Section: Negotiation Protocolmentioning

confidence: 99%

Automated Negotiations Under User Preference Uncertainty: A Linear Programming Approach

Tsimpoukis¹,

Baarslag²,

Kaisers³

et al. 2019

Agreement Technologies

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Autonomous agents negotiating on our behalf find applications in everyday life in many domains such as high frequency trading, cloud computing and the smart grid among others. The agents negotiate with one another to reach the best agreement for the users they represent. An obstacle in the future of automated negotiators is that the agent may not always have a priori information about the preferences of the user it represents. The purpose of this work is to develop an agent that will be able to negotiate given partial information about the user's preferences. First, we present a new partial information model that is supplied to the agent, which is based on categorical data in the form of pairwise comparisons of outcomes instead of precise utility information. Using this partial information, we develop an estimation model that uses linear optimization and translates the information into utility estimates. We test our methods in a negotiation scenario based on a smart grid cooperative where agents participate in energy trade-offs. The results show that already with very limited information the model becomes accurate quickly and performs well in an actual negotiation setting. Our work provides valuable insight into how uncertainty affects an agent's negotiation performance, how much information is needed to be able to formulate an accurate user model, and shows a capability of negotiating effectively with minimal user feedback.

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Computers That Negotiate on Our Behalf: Major Challenges for Self-sufficient, Self-directed, and Interdependent Negotiating Agents

Cited by 13 publications

References 60 publications

Automated peer-to-peer negotiation for energy contract settlements in residential cooperatives

Automated peer-to-peer negotiation for energy contract settlements in residential cooperatives

Challenges and Main Results of the Automated Negotiating Agents Competition (ANAC) 2019

Automated Negotiations Under User Preference Uncertainty: A Linear Programming Approach

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