Multi-agent system

About: Multi-agent system is a research topic. Over the lifetime, 27978 publications have been published within this topic receiving 465191 citations. The topic is also known as: multi-agent systems & multiagent system.

Genetic network programming - application to intelligent agents

Abstract: Recently many studies have been made on the automatic design of complex systems using evolutionary optimization techniques such as genetic algorithms (GA), evolution strategy (ES), evolutionary programming (EP) and genetic programming (GP). It is generally recognized that these techniques are very useful for optimizing fairly complex systems such as the generation of intelligent behavior sequences of robots. A new method, genetic network programming (GNP), is proposed in order to acquire these behavior sequences efficiently. GNP is composed of plural nodes for agents to execute simple judgment/processing and they are connected with each other to form a network structure. Agents behave according to the contents of the nodes and their connections in GNP. In order to obtain a better structure, the GNP changes itself using evolutionary optimization techniques.

Dynamic Event-Triggered Control for Leader-Following Consensus of Multiagent Systems

Abstract: In this paper, the leader-following consensus problem of multiagent systems with general dynamics under event-triggered mechanisms is investigated. A centralized event-triggered mechanism (CEM) is first proposed. Then, a distributed dynamic event-triggered mechanism is developed by introducing an internal variable. In the CEM case, each agent uses global information of the multiagent system, while in the distributed case, each agent only uses local information of its own and its neighbors. The multiagent system can achieve asymptotic consensus as well as exclude the Zeno phenomenon under the designed event-triggered rules in both cases. A multiagent system consisting of interconnected pendulums is provided to demonstrate the merits and correctness of the proposed methods.

Modeling Complex Multi-Issue Negotiation Using Utility Graphs

Abstract: Automated bilateral negotiation forms an important type of interaction in agent based systems for electronic commerce; it allows seller and customer to determine the terms and content of the trade iteratively and bilaterally. Consequently, deals may be highly customized (especially for complex goods or services) and highly adaptable to changing circumstances. Moreover, by automating the negotiation process, the potentially time-consuming process is delegated to autonomous software agents who conduct the actual negotiation on behalf of their owners. In this paper, we consider the problem of a seller agent negotiating bilaterally with a customer about selecting a subset from a collection of goods or services, viz. the bundle, together with a price for that bundle. The techniques developed in this paper try to benefit from the so-called win-win opportunities, by finding mutually beneficial alternative bundles during negotiations. To facilitate the search for win-win opportunities the developed techniques rely on a continuously updated model of the opponent's preferences. Several papers on multiagent negotiation have already focused on finding win-win opportunities through opponent modeling. However, these papers only consider preference relations for which the issues have independent valuations. In this paper we study the considerably harder problem of interdependencies between issues. In order to model such complex interdependencies between items, we introduce the novel concept of utility graphs. Utility graphs build on the idea that highly nonlinear utility functions, which are not decomposable in sub-utilities of individual items (such as in the seminal work of Raiffa), may be decomposable in sub-utilities of clusters of inter-related items. They mirror, to a certain extent, the graphical models developed in (Bayesian) inference theory. The idea, behind using utility graphs in a one multi-issue bargaining setting, is to provide the seller with a formalism for exploring the exponentially large bundle space, efficiently. In this paper, we show how utility graphs can be used to model an opponent's (i.e. customer's) preferences. Moreover, we also propose an updating procedure to obtain approximations of the customer s utility graph indirectly, by only observing his counter-offers during the negotiation. At the start of a negotiation process, the seller's approximation of the customer's utility graph represents some prior information about the maximal structure of the utility space to be explored. This prior information could be obtained through a history of past negotiations or the input of domain experts. (An important advantage of utility graphs is that they can handle both qualitative and quantitative prior information.) After every (counter) offer of the customer, this approximation is refined. Conducted computer experiments show that by using only a fairly weak assumption on the maximal structure of customers utility functions the updating procedure enables the seller to suggest offers that closely approximate Pareto efficiency. By using utility graphs, Pareto-efficiency can be reached with few negotiation steps, because we explicitly model the underlying graphical structure of complex utility functions of the Buyer and use it to explore the outcome space. Consequently, our approach is applicable to time constrained negotiations, or negotiations where the impatience of one of the parties is a limiting factor. Furthermore, unlike other solutions for high-dimensional negotiations, the proposed approach does not require a mediator.

MAGENTA technology: multi-agent systems for industrial logistics

Abstract: We introduce MAGENTA'S commercial multi-agent systems technology, and illustrate its practical use by describing a field-tested application in the area of logistics/scheduling. MAGENTA technology provides two integrated toolsets for building industrial-strength systems: the Ontology Management Toolkit (which enables designers to capture the concepts and interrelationships between concepts in an application), and the Virtual Marketplace Engine (the platform supporting agent interaction in MAGENTA'S technology); in addition, run-time visualisation and monitoring tools are provided for debugging systems. The application we describe is a field-tested scheduling/logistics system for Tankers International, which provides intelligent support in the scheduling of a 46-strong Very Large Crude Carrier (VLCC) fleet.

Multi-Agent Systems

Abstract: We review the state of the art in knowledge representation formalisms for multi-agent systems. We divide work in this area into two categories. In the first category are approaches that attempt to represent the cognitive state of rational agents, and to characterize logically how such a state leads a rational agent to act. We begin by motivating this approach. We then describe four of the best-known such logical frameworks, and discuss the possible roles that such logics can play in helping us to engineer artificial agents. In the second category are approaches based on representing the strategic structure of a multi-agent environment, and in particular, the powers that agents have, either individually or in coalitions. Here, we describe Coalition Logic, Alternatingtime Temporal Logic (ATL), and epistemic extensions.