Unconventional Negotiation: Survey and New Directions

The increasing demand for building large-scale complex and distributed systems such as Cloud/Grid computing systems accentuates the need for complex negotiation mechanisms for managing computing resources. The contribution of this paper includes: 1) summarizing classical negotiation problems and conventional negotiation in terms of the utility function, strategy, and protocol, 2) discussing the differences between conventional negotiation and unconventional negotiation, 3) reviewing and comparing the state-of-the-art developments in both relaxed-criteria negotiation, and complex and concurrent negotiation, and 4) suggesting new directions in complex negotiation and its applications

An Evolutionary Learning Approach for Adaptive Negotiation Agents

Developing effective and efficient negotiation mechanisms for real-world applications such as e-Business is challenging since negotiations in such a context are characterised by combinatorially complex negotiation spaces, tough deadlines, very limited information about the opponents, and volatile negotiator preferences. Accordingly, practical negotiation systems should be empowered by effective learning mechanisms to acquire dynamic domain knowledge from the possibly changing negotiation contexts. This paper illustrates our adaptive negotiation agents which are underpinned by robust evolutionary learning mechanisms to deal with complex and dynamic negotiation contexts. Our experimental results show that GA-based adaptive negotiation agents outperform a theoretically optimal negotiation mechanism which guarantees Pareto optimal. Our research work opens the door to the development of practical negotiation systems for real-world applications

Tasks for Agent-Based Negotiation Teams:Analysis, Review, and Challenges

An agent-based negotiation team is a group of interdependent agents that join together as a single negotiation party due to their shared interests in the negotiation at hand. The reasons to employ an agent-based negotiation team may vary: (i) more computation and parallelization capabilities, (ii) unite agents with different expertise and skills whose joint work makes it possible to tackle complex negotiation domains, (iii) the necessity to represent different stakeholders or different preferences in the same party (e.g., organizations, countries, and married couple). The topic of agent-based negotiation teams has been recently introduced in multi-agent research. Therefore, it is necessary to identify good practices, challenges, and related research that may help in advancing the state-of-the-art in agent-based negotiation teams. For that reason, in this article we review the tasks to be carried out by agent-based negotiation teams. Each task is analyzed and related with current advances in different research areas. The analysis aims to identify special challenges that may arise due to the particularities of agent-based negotiation teams.Comment: Engineering Applications of Artificial Intelligence, 201

Evolving the best-response strategy to decide when to make a proposal

Abstract-This paper designed and developed negotiation agents with the distinguishing features of 1) conducting continuous time negotiation rather than discrete time negotiation, 2) learning the response times of trading parties using Bayesian learning and, 3) deciding when to make a proposal using a multi-objective genetic algorithm (MOGA) to evolve their best-response proposing time strategies for different negotiation environments and constraints. Results from a series of experiments suggest that 1) learning trading parties' response times helps agents achieve more favorable trading results, and 2) on average, when compared with SSAs (Static Strategy Agents), BRSAs (Best-Response proposing time Strategy Agents) achieved higher average utilities, higher success rates in reaching deals, and smaller average negotiation time

Agents that react to changing market situations

Abstract—Market-driven agents are negotiation agents that react to changing market situations by making adjustable rates of concession. This paper presents 1) the foundations for designing market-driven strategies of agents, 2) a testbed of market-driven agents, 3) experimental results in simulating the market-driven approach, and 4) theoretical analyses of agents ’ performance in extremely large markets. In determining the amount of concession for each trading cycle, market-driven agents in this research are guided by four mathematical functions of eagerness, remaining trading time, trading opportunity, and competition. At different stages of trading, agents may adopt different trading strategies, and make different rates of concession. Four classes of strategies with respect to remaining trading time are discussed. Trading opportunity is determined by considering: 1) number of trading partners, 2) spreads—differences in utilities between an agent and its trading partners, and 3) probability of completing a deal. While eagerness represents an agent’s desire to trade, trading competition is determined by the probability that it is not considered as the most preferred trader by its trading partners. Experimental results and theoretical analyses showed that agents guided by market-driven strategies 1) react to changing market situations by making prudent and appropriate rates of concession, and 2) achieve trading outcomes that correspond to intuitions in real-life trading. Index Terms—Negotiation agent, reactive agent. I