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

Rosenfeld, Avi; Zuckerman, Inon; Segal-Halevi, Erel; Drein, Osnat; Kraus, Sarit

doi:10.1007/s10458-015-9281-9

Cited by 42 publications

(24 citation statements)

References 31 publications

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“…Several efforts have advanced the design of conversational agents that negotiate with human users, both as a means for teaching negotiation skills [1] and as a means to advance the social intelligence of virtual agents [2,3]. Negotiation engages a wide range of individual and interpersonal skills that are not only crucial for people, but essential for machines that socially engage with humans.…”

Section: Introductionmentioning

confidence: 99%

Opponent Modeling for Virtual Human Negotiators

Nazari

Lucas

Gratch

2015

Lecture Notes in Computer Science

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Abstract. Negotiation is a challenging domain for virtual human research. One aspect of this problem, known as opponent modeling, is discovering what the other party wants from the negotiation. Research in automated negotiation has yielded a number opponent modeling techniques but we show that these methods do not easily transfer to human-agent settings. We propose a more effective heuristic for inferring preferences both from a negotiator's pattern of offers and verbal statements about their preferences. This method has the added advantage that it can detect negotiators that lie about their preferences. We discuss several ways the method can enhance the capabilities of a virtual human negotiator.

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Section: Introductionmentioning

confidence: 99%

Opponent Modeling for Virtual Human Negotiators

Nazari

Lucas

Gratch

2015

Lecture Notes in Computer Science

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“…However, in practical applications not all people will choose agents to negotiate on their behalf. As a result, exploring human avatars in GOLEM to negotiate with software agents will be relevant and works such that of Rosenfeld et al [51] to provide an agent that is able to negotiate with agents and humans at the same time will be key. For this we will need to create a flexible negotiation language for an agent to interact with both artificial and human agents alike.…”

Section: Discussionmentioning

confidence: 99%

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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“…In this envisioned line of research, each negotiation party consists of at least one human and one negotiation agent. The agent should do the brunt of the negotiation work to find possible agreements with the other negotiation parties and which can presented to their human partners for feedback and new input, which necessitates an understanding of their behavior [33], attitudes [77], and preferred interaction method [61]. The research challenge is to determine when, how, and how often to switch the initiative from human to agent and vice versa.…”

Section: User Trust and Adoptionmentioning

confidence: 99%

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

Baarslag

Kaisers

Gerding

et al. 2017

Autonomous Agents and Multiagent Systems

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Abstract.Computers that negotiate on our behalf hold great promise for the future and will even become indispensable in emerging application domains such as the smart grid, autonomous driving, and the Internet of Things. Much research has thus been expended to create agents that are able to negotiate in an abundance of circumstances. However, up until now, truly autonomous negotiators have rarely been deployed in realworld applications. This paper sizes up current negotiating agents and explores a number of technological, societal and ethical challenges that autonomous negotiation systems are bringing about. The questions we address are: in what sense are these systems autonomous, what has been holding back their further proliferation, and is their spread something we should encourage? We relate the automated negotiation research agenda to dimensions of autonomy and distill three major themes that we believe will propel autonomous negotiation forward: accurate representation, long-term perspective, and user trust. We argue these orthogonal research directions need to be aligned and advanced in unison to sustain tangible progress in the field.

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

Cited by 42 publications

References 31 publications

Opponent Modeling for Virtual Human Negotiators

Opponent Modeling for Virtual Human Negotiators

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

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

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