Metaphor-based negotiation and its application in AGV movement planning

The theme of this thesis is "metaphor-based negotiation". By metaphor-based negotiation I mean a category of approaches for problem-solving in Distributed Artificial Intelligence (DAI) that mimic some aspects of human negotiation behaviour. The research in this dissertation is divided into two closely related parts. Cooperative interaction among agents in a multiagent system (MAS) is discussed in general, and the discussion leads to a formal definition of metaphor-based negotiation. Then, as a specific application, a "spring-based" computational model for metaphor-based negotiation is developed as an approach to solving movement planning, specifically the AGV scheduling problem (AGVSP) — determing the timings of AGVs' activities, of automated guided vehicles (AGVs) in a factory.By formally addressing the multi-agent cooperative interaction problem and assuming that agents in a MAS are rational, benevolent and fully informed, an initial strategy set of cooperative interaction can be reduced to a strategy set by eliminating strategies that are irrational in a group sense. However, it is proved in this dissertation that, in the remaining strategy set, no unique strategy can be found that is acceptable to all agents according their individual preferences. More specifically, in this smaller strategy set, if one agent moves from one strategy to another in an attempt to better its individual goal achievement, then there is at least one agent whose goal achievement will be negatively affected by such a move. So, the cooperative interaction problem can only be partially solved if no further knowledge is given to those agents. The idea of a common sense principle is introduced in this dissertation to overcome the deficiencies of the assumptions of rationality, benevolence and full-informedness.In reality, the assumption of full-informedness of agents may not be practical. Communication is needed for agents to (1) exchange their local problem solving information, and (2) exchange proposals for global problem solving, when their views are in conflict. Based on the discussion of cooperative interaction, a formal definition of metaphorbased negotiation is proposed to formally indicate what is a proposal and what is the condition for accepting a proposal from another agent. In this definition, the common sense principle is one of the most important features, not found in definitions of negotiation available so far in the literature, which guides agents to find an agreement when negotiation is running into difficulties.The AGVSP involves timing activities for each AGV in a AGV-based factory. The AGVSP is naturally distributed: the whole problem can be easily divided into several subproblems each of which involves timing of activities of one AGV. Therefore, it is intuitively straightforward for us to seek DAI approaches to solving the AGVSP. In spired by Kwa's Iterative Negotiation Model [Kwa 88b] [Kwa 88a] for the AGVSP, we developed a spring-based (metaphor-based) negotiation model for the AGVSP to overcome some vital problems in Kwa's model. The idea of the spring-based negotiation model is described below:The AGVSP can be regarded as a Distributed Constraint Satisfaction Problem (DCSP) and solved in a MAS. Each agent in the MAS is designed to solve a subproblem — a local scheduling problem which is a small Constraint Satisfaction Problem (CSP). Conflicts exist when intra-agent constraints or inter-agent constraints are violated. These constraints can be classified into hard constraints— those that can not be relaxed at the agent level unless the system designer permits (e.g., by providing an arbitrator), and soft constraints — those that can be relaxed at the agent level when necessary. When agents are in conflict, i.e, when some inter-agent constraints are violated (or say, when one agent's timings of its activities overlap those of some other agents), these agents involved will resolve the conflicts through a (metaphor-based) negotiation procedure in which conflicts will be gradually resolved by each agent's relaxation of its intra-agent constraints, i.e, by yielding some amount of its initially allocated resources to other agents or by shifting its initially allocated resources. The negotiation can be viewed as a process of exchanging proposals (of cooperative strategies) between conflicting agents, where a cooperative strategy is a possible resolution to a conflict according to the viewpoint of the proposing agent. However, since agents are designed to be rational, each agent that is involved in the conflicts will try hard to relax its intra-agent constraints as little as possible. Further, it is reasonably acceptable that the more an intra-agent constraint has been relaxed the less the respective agent is willing to relax it further. This feature can be modeled by a spring — the more it has been compressed the harder it is to compress it further. Based on this inspiration, a spring-based computational model of metaphor-based negotiation is proposed: each agent's local schedule is represented by a local spring network in which each spring element represents a soft intra-agent constraint. Relaxation of an intra-agent constraint is likened to a spring being compressed by external forces from other agents. As a consequence, the compressed spring will also show a reacting force upon those compressing agents. An agreement will be reached when those forces and reacting forces are balanced. This is the common sense principle in the spring-based negotiation. The model solves some key issues, e.g., how to select negotiation techniques and skills during the process of negotiation, that have not been solved by Kwa's iterative negotiation model. Some experimental evidence of the value of this model is presented

A distributed knowledge-based approach to flexible automation : the contract-net framework

Includes bibliographical references (p. 26-29)

Theories about architecture and performance of multi-agent systems

Multi-agent systems are promising as models of organization because they are based on the idea that most work in human organizations is done based on intelligence, communication, cooperation, and massive parallel processing. They offer an alternative for system theories of organization, which are rather abstract of nature and do not pay attention to the agent level. In contrast, classical organization theories offer a rather rich source of inspiration for developing multi-agent models because of their focus on the agent level. This paper studies the plausibility of theoretical choices in the construction of multi-agent systems. Multi-agent systems have to be plausible from a philosophical, psychological, and organizational point of view. For each of these points of view, alternative theories exist. Philosophically, the organization can be seen from the viewpoints of realism and constructivism. Psychologically, several agent types can be distinguished. A main problem in the construction of psychologically plausible computer agents is the integration of response function systems with representational systems. Organizationally, we study aspects of the architecture of multi-agent systems, namely topology, system function decomposition, coordination and synchronization of agent processes, and distribution of knowledge and language characteristics among agents. For each of these aspects, several theoretical perspectives exist.

Software Agents

being used, and touted, for applications as diverse as personalised information management, electronic commerce, interface design, computer games, and management of complex commercial and industrial processes. Despite this proliferation, there is, as yet, no commonly agreed upon definition of exactly what an agent is — Smith et al. (1994) define it as “a persistent software entity dedicated to a specific purpose”; Selker (1994) takes agents to be “computer programs that simulate a human relationship by doing something that another person could do for you”; and Janca (1995) defines an agent as “a software entity to which tasks can be delegated”. To capture this variety, a relatively loose notion of an agent as a self-contained program capable of controlling its own decision making and acting, based on its perception of its environment, in pursuit of one or more objectives will be used here. Within the extant applications, three distinct classes of agent can be identified. At the simplest level, there are “gopher ” agents, which execute straightforward tasks based on pre-specified rules and assumptions (eg inform me when the share price deviates by 10 % from its mean position or tell me when I need to reorder stock items). The next level of sophistication involves “service performing” agents, which execute a well defined task at the request of a user (eg find me the cheapest flight to Paris or arrange a meeting with the managing director some day next week). Finally, there are “predictive ” agents, which volunteer information or services to a user, without being explicitly asked, whenever it is deemed appropriate (eg an agent may monitor newsgroups on the INTERNET and return discussions that it believes to be of interest to the user or a holiday agent may inform its user that a travel firm is offering large discounts on holidays to South Africa knowing that the user is interested in safaris). Common to all these classes are the following key hallmarks of agenthoo

On Agent-Based Software Engineering

Agent-based computing represents an exciting new synthesis both for Artificial Intelligence (AI) and, more generally, Computer Science. It has the potential to significantly improve the theory and the practice of modeling, designing, and implementing computer systems. Yet, to date, there has been little systematic analysis of what makes the agent-based approach such an appealing and powerful computational model. Moreover, even less effort has been devoted to discussing the inherent disadvantages that stem from adopting an agent-oriented view. Here both sets of issues are explored. The standpoint of this analysis is the role of agent-based software in solving complex, real-world problems. In particular, it will be argued that the development of robust and scalable software systems requires autonomous agents that can complete their objectives while situated in a dynamic and uncertain environment, that can engage in rich, high-level social interactions, and that can operate within flexible organisational structures

The Semantic Grid: A future e-Science infrastructure

e-Science offers a promising vision of how computer and communication technology can support and enhance the scientific process. It does this by enabling scientists to generate, analyse, share and discuss their insights, experiments and results in an effective manner. The underlying computer infrastructure that provides these facilities is commonly referred to as the Grid. At this time, there are a number of grid applications being developed and there is a whole raft of computer technologies that provide fragments of the necessary functionality. However there is currently a major gap between these endeavours and the vision of e-Science in which there is a high degree of easy-to-use and seamless automation and in which there are flexible collaborations and computations on a global scale. To bridge this practice–aspiration divide, this paper presents a research agenda whose aim is to move from the current state of the art in e-Science infrastructure, to the future infrastructure that is needed to support the full richness of the e-Science vision. Here the future e-Science research infrastructure is termed the Semantic Grid (Semantic Grid to Grid is meant to connote a similar relationship to the one that exists between the Semantic Web and the Web). In particular, we present a conceptual architecture for the Semantic Grid. This architecture adopts a service-oriented perspective in which distinct stakeholders in the scientific process, represented as software agents, provide services to one another, under various service level agreements, in various forms of marketplace. We then focus predominantly on the issues concerned with the way that knowledge is acquired and used in such environments since we believe this is the key differentiator between current grid endeavours and those envisioned for the Semantic Grid

HOMEBOTS: Intelligent Decentralized Services for Energy Management

The deregulation of the European energy market, combined with emerging advanced capabilities of information technology, provides strategic opportunities for new knowledge-oriented services on the power grid. HOMEBOTS is the namewe have coined for one of these innovative services: decentralized power load management at the customer side, automatically carried out by a `society' of interactive household, industrial and utility equipment. They act as independent intelligent agents that communicate and negotiate in a computational market economy. The knowledge and competence aspects of this application are discussed, using an improved \ud version of task analysis according to the COMMONKADS knowledge methodology. Illustrated by simulation results, we indicate how customer knowledge can be mobilized to achieve joint goals of cost and energy savings. General implications for knowledge creation and its management are discussed