GENIE: A decision support system for crisis negotiations

Wilkenfeld, Jonathan; Kraus, Sarit; Holley, Kim M.; Harris, Michael A.

doi:10.1016/0167-9236(94)00027-p

Cited by 49 publications

(18 citation statements)

References 27 publications

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“…We believe that maximizing these other values can be critical-especially if the system's goal is to support a person's decision-making process or to help both sides reach better agreements. For example, the GENIE and Pocket Negotiator projects [44,45] focused on how to use an agent as decision support during a person's negotiation session. AutoMed and AniMed* are agents that act as a mediator between bilateral negotiations between people [46,47].…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

NegoChat-A: a chat-based negotiation agent with bounded rationality

Rosenfeld

Zuckerman

Segal-Halevi

et al. 2015

Auton Agent Multi-Agent Syst

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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 typically lack the natural language processing support required to enable real-world types of interactions. To address this limitation, we present NegoChat-A, an agent that incorporates several significant research contributions. First, we found that simply modifying existing agents to include an natural language processing module is insufficient to create these agents. Instead, agents that support natural language must have strategies that allow for partial agreements and issue-by-issue interactions. Second, we present NegoChat-A's negotiation algorithm. This algorithm is based on bounded rationality, and specifically anchoring and aspiration adaptation theory. The agent begins each negotiation interaction by proposing a full offer, which serves as its anchor. Assuming this offer is not accepted, the agent then proceeds to negotiate via partial agreements, proposing the next issue for negotiation based on people's typical urgency, or order of importance. We present a rigorous evaluation of NegoChat-A, showing its effectiveness in two different negotiation roles.

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Section: Discussion and Future Directionsmentioning

confidence: 99%

NegoChat-A: a chat-based negotiation agent with bounded rationality

Rosenfeld

Zuckerman

Segal-Halevi

et al. 2015

Auton Agent Multi-Agent Syst

Self Cite

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show abstract

“…Negotiations are ubiquitous and conducted in various contexts such as the formation of virtual enterprises [7], marketing, establishing business contracts, managing labour disputes [26], resolving border conflicts, handling hostage crisis [29], etc. Negotiation refers to the process by which group of agents (human or software) communicate with one another in order to reach a mutually acceptable agreement on resource allocation (distribution) [16].…”

Section: The Problemsmentioning

confidence: 99%

Mining Negotiation Knowledge for Adaptive Negotiation Agents in e-Marketplaces

Lau

Wong

2007

2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07)

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By increasing the degree and sophistication of automation, e-marketplaces will become much more efficient and transparent, and hence more widely adopted by organizations. Negotiation is one of main activities conducted in e-marketplaces, and adaptive negotiation agents can be applied to improve the effectiveness of B2B e-marketplaces. Classical negotiation models have limited use in modern e-marketplaces because these models often assume that complete information about the negotiation spaces is available. This paper illustrates the design and development of adaptive negotiation agents for e-marketplaces. These agents are empowered by the Bayesian learning mechanisms so that they can gradually acquire negotiation knowledge based on their previous encounters with the opponents. Our preliminary experiment shows that the proposed probabilistic negotiation decision making mechanism and the associated data mining approach is effective and efficient in simulated e-marketplaces.

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“…Subsequent work using distributed artificial intelligence techniques have enabled NEGOPLAN to provide more sophisticated generic negotiation advice. GENIE [24] integrates rule-based reasoning and multi-attribute analysis to offer decision support aimed at resolving international disputes. MEDIATOR [l I] used case retrieval and adaptation to propose solutions to international disputes.…”

Section: A Survey Of Artificialmentioning

confidence: 99%

AI techniques for modelling legal negotiation

Bellucci

Zeleznikow

1999

Proceedings of the 7th International Conference on Artificial Intelligence and Law

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of participants, context and user preferences.Negotiation is a process of cooperative decision-making between parties concerning the resolution of a common dispute. The goal of negotiation is to develop a settlement that is acceptable to both parties. One way to achieve this is to ensure disputants take responsibility for their outcomes and control the disputation process.Most current systems have chosen to model the negotiation process by representing progress made within a negotiation. In this paper we focus on modelling the trade-offs and compromises to made by parties to a legal dispute. A system we have built to model disputes in Australian Family Law uses fuzzy networks, numerical allocation procedures and decomposition hierarchies.The most powerful intelligent negotiation support systems are domain dependent, and it is unclear how their techniques might be made applicable in other domains. The reason for this is that negotiation itself is very context dependent. Negotiation is also a process dependent on the requirements of its working domain. The domain we discuss in this paper is Australian Family Law. In most legal conflicts, once a settlement is reached the parties to the settlement are not required to have an on-going relationship. This is not the case in Australian Family Law. Family law varies from other legal domains in that in general:

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GENIE: A decision support system for crisis negotiations

Cited by 49 publications

References 27 publications

NegoChat-A: a chat-based negotiation agent with bounded rationality

NegoChat-A: a chat-based negotiation agent with bounded rationality

Mining Negotiation Knowledge for Adaptive Negotiation Agents in e-Marketplaces

AI techniques for modelling legal negotiation

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