Multi-agent data mining with negotiation: a study in multi-agent based clustering

Multi-Agent Data Mining (MADM) seeks to harness the general advantages offered by Multi-Agent System (MAS) with respect to the domain of data mining. The research described in this thesis is concerned with Multi-Agent Based Clustering (MABC), thus MADM to support clustering. To investigate the use of MAS technology with respect to data mining, and specifically data clustering, two approaches are proposed in this thesis. The first approach is a multi-agent based approach to clustering using a generic MADM framework whereby a collection of agents with different capabilities are allowed to collaborate to produce a ``best'' set of clusters. The framework supports three clustering paradigms: K-means, K-NN and divisive hierarchical clustering. A number of experiments were conducted using benchmark UCI data sets and designed to demonstrate that the proposed MADM approach can identify a best set of clusters using the following clustering metrics: F-measure, Within Group Average Distance (WGAD) and Between Group Average Distance (BGAD). The results demonstrated that the MADM framework could successfully be used to find a best cluster configuration. The second approach is an extension of the proposed initial MADM framework whereby a ``best'' cluster configuration could be found using cooperation and negotiation among agents. The novel feature of the extended framework is that it adopts a two-phase approach to clustering. Phase one is similar to the established centralised clustering approach (except that it is conducted in a decentralised manner). Phase two comprises a negotiation phase where agents ``swap'' unwanted records so as to improve a cluster configuration. A set of performatives is proposed as part of a negotiation protocol to facilitate intra-agent negotiation. It is this negotiation capability which is the central contribution of the work described in this thesis. An extensive evaluation of the extended framework was conducted using: (i) benchmark UCI data sets and (ii) a welfare benefits data set that provides an exemplar application. Evaluation of the framework clearly demonstrates that, in the majority of cases, this negotiation phase serves to produce a better cluster configuration (in terms of cohesion and separation) than that produced using a simple centralised approach

Negotiating Concurrently with Unknown Opponents in Complex, Real-Time Domains

We propose a novel strategy to enable autonomous agents to negotiate concurrently with multiple, unknown opponents in real-time, over complex multi-issue domains. We formalise our strategy as an optimisation problem, in which decisions are based on probabilistic information about the opponents' strategies acquired during negotiation. In doing so, we develop the first principled approach that enables the coordination of multiple, concurrent negotiation threads for practical negotiation settings. Furthermore, we validate our strategy using the agents and domains developed for the International Automated Negotiating Agents Competition (ANAC), and we benchmark our strategy against the state-of-the-art. We find that our approach significantly outperforms existing approaches, and this difference improves even further as the number of available negotiation opponents and the complexity of the negotiation domain increases

Negotiation in Multi-Agent Systems

In systems composed of multiple autonomous agents, negotiation is a key form of interaction that enables groups of agents to arrive at a mutual agreement regarding some belief, goal or plan, for example. Particularly because the agents are autonomous and cannot be assumed to be benevolent, agents must influence others to convince them to act in certain ways, and negotiation is thus critical for managing such inter-agent dependencies. The process of negotiation may be of many different forms, such as auctions, protocols in the style of the contract net, and argumentation, but it is unclear just how sophisticated the agents or the protocols for interaction must be for successful negotiation in different contexts. All these issues were raised in the panel session on negotiation

Arguing and negotiating in the presence of social influences

Postprin

Human-Agent Decision-making: Combining Theory and Practice

Extensive work has been conducted both in game theory and logic to model strategic interaction. An important question is whether we can use these theories to design agents for interacting with people? On the one hand, they provide a formal design specification for agent strategies. On the other hand, people do not necessarily adhere to playing in accordance with these strategies, and their behavior is affected by a multitude of social and psychological factors. In this paper we will consider the question of whether strategies implied by theories of strategic behavior can be used by automated agents that interact proficiently with people. We will focus on automated agents that we built that need to interact with people in two negotiation settings: bargaining and deliberation. For bargaining we will study game-theory based equilibrium agents and for argumentation we will discuss logic-based argumentation theory. We will also consider security games and persuasion games and will discuss the benefits of using equilibrium based agents.Comment: In Proceedings TARK 2015, arXiv:1606.0729

Mechanisms for Automated Negotiation in State Oriented Domains

This paper lays part of the groundwork for a domain theory of negotiation, that is, a way of classifying interactions so that it is clear, given a domain, which negotiation mechanisms and strategies are appropriate. We define State Oriented Domains, a general category of interaction. Necessary and sufficient conditions for cooperation are outlined. We use the notion of worth in an altered definition of utility, thus enabling agreements in a wider class of joint-goal reachable situations. An approach is offered for conflict resolution, and it is shown that even in a conflict situation, partial cooperative steps can be taken by interacting agents (that is, agents in fundamental conflict might still agree to cooperate up to a certain point). A Unified Negotiation Protocol (UNP) is developed that can be used in all types of encounters. It is shown that in certain borderline cooperative situations, a partial cooperative agreement (i.e., one that does not achieve all agents' goals) might be preferred by all agents, even though there exists a rational agreement that would achieve all their goals. Finally, we analyze cases where agents have incomplete information on the goals and worth of other agents. First we consider the case where agents' goals are private information, and we analyze what goal declaration strategies the agents might adopt to increase their utility. Then, we consider the situation where the agents' goals (and therefore stand-alone costs) are common knowledge, but the worth they attach to their goals is private information. We introduce two mechanisms, one 'strict', the other 'tolerant', and analyze their affects on the stability and efficiency of negotiation outcomes.Comment: See http://www.jair.org/ for any accompanying file

Automated Bilateral Bargaining about Multiple Attributes in a One­ to ­Many Setting

Negotiations are an important way of reaching agreements between selfish autonomous agents. In this paper we focus on one-to-many bargaining within the context of agent-mediated electronic commerce. We consider an approach where a seller agent negotiates over multiple interdependent attributes with many buyer agents in a bilateral fashion. In this setting, "fairness", which corresponds to the notion of envy-freeness in auctions, may be an important business constraint. For the case of virtually unlimited supply (such as information goods), we present a number of one-to-many bargaining strategies for the seller agent, which take into account the fairness constraint, and consider multiple attributes simultaneously. We compare the performance of the bargaining strategies using an evolutionary simulation, especially for the case of impatient buyers. Several of the developed strategies are able to extract almost all the surplus; they utilize the fact that the setting is one-to-many, even though bargaining is bilateral