In dynamic multiagent systems, self-motivated agents pursuing individual goals may interfere with each other's plans. Agents must, therefore, coordinate their plans to resolve dependencies among them. This drives the need for agents to engage in dialogue to decide what to do in collaboration. Agreeing what to do is a complex activity, however, when agents come to an encounter with different objectives and norm expectations (i.e. societal norms that constrain acceptable behaviour). Argumentation-based models of dialogue support agents in deciding what to do analysing pros/cons for decisions, and enable conflict resolution by revealing structured background information that facilitates the identification of acceptable solutions. Existing models of deliberative dialogue, however, commonly assume that agents have a shared goal, and to date their effectiveness has been shown only through the use of extended examples. In this research, we propose a novel model of argumentation schemes to be integrated in a dialogue for the identification of plan, goal and norm conflicts when agents have individual but interdependent objectives. We empirically evaluate our model within a dynamic system to establish how the information shared with argumentation schemes influence dialogue outcomes. We show that by employing our model of arguments in dialogue, agents achieve more successful agreements. The resolution of conflicts and identification of more feasible interdependent plans is achieved through the sharing of focussed information driven by argumentation schemes. Agents may also consider more important conflicts, or conflicts that cause higher loss of utility if unresolved. We explore the use of strategies for agents to select arguments that are more likely to solve important conflicts.

Thesis (Ph. D.)--Oregon State University, 2010. / Printout. Includes bibliographical references (leaves 121-123). Also available on the World Wide Web.

Thesis (Ph.D.)--Kent State University, 2008. / Title from PDF t.p. (viewed April 16, 2010). Advisor: Marvin Troutt. Keywords: ABM; agent; repast; information sharing. Includes bibliographical references (p. 161-179).

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Managing large scale supply chains are never an easy task. Numerous researches have put emphasis on supply chain modeling and optimization to assist businesses in searching for the best practices so as to endure the extremely competitive business landscape. To some, the paradigm of centralized supply chain management is adequate for solving its strategic and operational problems. Yet with the improper use of authoritative assumptions, the efficiency of the management process is often jeopardized. Furthermore, current researches in decentralized supply chain are mostly focused on dyadic or linear relationship and seldom consider quantitative modeling and analysis with scalability. Recent development in multi-agent systems provided a means for such a modeling methodology and hence researches in this area. To enhance model representativeness and computational efficiency, vision-based control models that are able to simulate individual operational and strategic traits are developed. In this research, pyramidal agent alignment is proposed for simulating the management-operation dimension with regards to decision exercising and bargaining power management. The system offers one thousand supply chain agents that are simulated in a mono-layer, multi-tier network in real time. Stochastic and dynamic behaviors of the network are handled by statistical regression on scenario-based model evaluation. The proposed design enabled grand scale supply chain modeling and optimization that follows a general or custom simulation supported optimization architecture. Network governance problems and dynamic steering problems are considered and solved using genetic algorithm and dynamic programming. The thesis looks into the potential benefits and limitations of the proposed methods in details, and future research directions are discussed. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

Ph.D. (Computer Science) / The development of software systems is a relatively recent field of human endeavour. Even so, it has followed a steady progression of dominant paradigms which have incrementally improved the ease with which developers are able to express the logic and structure of their systems. The initially unstructured era of free-form spaghetti code gave way to structured programming in which the entry and exit points of functional units were well defined through the creation of abstractions such as procedures, sub-routines and functions. The problem of correctly associating data with the set of operations which are legal on this data was addressed through the concept of encapsulation with the onset of object-oriented programming. Object orientation also introduced a set of abstractions for safe code reuse through inheritance and dynamic polymorphism as well as composition/aggregation and delegation. The agent-oriented software development paradigm, when viewed as an extension of object orientation, adds the capacity of agent autonomy to an object, which allows it to select for itself which of its operations it will execute at any point in time. In addition, the separation between an agent and the environment within which it is embedded must be well defined. Agent autonomy allows for the modelling and development of loosely coupled systems with the capacity for complex emergent behaviour. The mapping of a given set of environmental percepts to an agent's operation selection defines its agent function and hence its emergent behaviour. Furthermore, agents may also be embedded into a shared environment together with other agents forming a multi-agent system. The emergent characteristics of such systems are defined not only through changes in environment state but also via agent to agent interactions. Multi-agent systems are categorised into cooperative or competitive based on whether all the agents within the system share a common goal. An argument is presented that even within cooperative multi-agent systems selfishness will emerge as a direct consequence of computational intractability. The core of the argument centres on the finite nature of the computational resources available to an agent which must be divided between the evaluation of the usefulness of other agent's knowledge and intentions towards improving the collective utility of the system and directly acting upon its own. As a direct result of the halting problem it is impossible for an agent to ascertain in general whether another agent's plans are even feasible (i.e. will result in the system reaching a goal state). As a direct consequence of such a limitation agents will in general favour their own courses of action over those of others and hence an emergent selfishness occurs even in ostensibly cooperative systems...

Through cooperation, agents can transcend their individual capabilities and achieve goals that would be unattainable otherwise. Existing multiagent planning work considers each agent’s action capabilities, but does not account for distributed knowledge and the incompatible views agents may have of the planning domain. These divergent views can be a result of faulty sensors, local and incomplete knowledge, and outdated information, or simply because each agent has conducted different inferences and their beliefs are not aligned. This thesis is concerned with Multi-Perspective Cooperative Planning (MPCP), the problem of synthesising a plan for multiple agents which share a goal but hold different views about the state of the environment and the specification of the actions they can perform to affect it. Reaching agreement on a mutually acceptable plan is important, since cautious autonomous agents will not subscribe to plans that they individually believe to be inappropriate or even potentially hazardous. We specify the MPCP problem by adapting standard set-theoretic planning notation. Based on argumentation theory we define a new notion of plan acceptability, and introduce a novel formalism that combines defeasible logic programming and situation calculus that enables the succinct axiomatisation of contradictory planning theories and allows deductive argumentation-based inference. Our work bridges research in argumentation, reasoning about action and classical planning. We present practical methods for reasoning and planning with MPCP problems that exploit the inherent structure of planning domains and efficient planning heuristics. Finally, in order to allow distribution of tasks, we introduce a family of argumentation-based dialogue protocols that enable the agents to reach agreement on plans in a decentralised manner. Based on the concrete foundation of deductive argumentation we analytically investigate important properties of our methods illustrating the correctness of the proposed planning mechanisms. We also empirically evaluate the efficiency of our algorithms in benchmark planning domains. Our results illustrate that our methods can synthesise acceptable plans within reasonable time in large-scale domains, while maintaining a level of expressiveness comparable to that of modern automated planning.

M.Sc. / The use of computer systems has changed over the years. Modern computer systems operate in an environment that is open, distributed and heterogeneous. They have the capability of locating information stored in remote locations and satisfying the interests and objectives of different users. However, the increase in user demands and the complexity of computers and information systems has caused research to focus on multi-agent systems as a solution to address these demands and complexities. The dissertation deals with the study of single agents and multi-agent systems. The study focuses on the concepts of agents, agent architecture and multi-agent systems. In addition to the study, a taxonomy for specialised agents is proposed. The taxonomy aims at classifying agent-based systems applied in the industry for addressing specific problems. In order to achieve this, a broad survey on agent-based systems in the industry was conducted. The areas under considerations were the financial, health, agricultural, aviation and the information technology sectors. The following dimensions were used to identify the agents in the specific area: • Which application domain is the multi-agent system designed for, developed and deployed in? • What is the specific task or problem the agents are designed to solve? • Do the agents have core or advanced agent attributes in general? The taxonomy is important because agent-based systems are becoming common in the industry and are suitable to address issues (such as locating distributed information and addressing specific needs of computer system users) of open, distributed and heterogeneous computer environments.

Coalition stability is an important concept in coalition formation. One common assumption in many stability criteria in non-transferable utility games is that the preference relations of each agent is publicly known so that a coalition is said to be stable if there is no objection by any sub-group of agents according to the publicly known preferences. / However, in many software agent applications, this assumption is not true. Instead, agents are modeled as individuals with private belief and decisions are made according to those beliefs instead of common knowledge. There are two types of uncertainty here. First, uncertainty in beliefs regarding the environment means that agents are also uncertain about their preferences. Second, an agent's actions can be influenced by his belief regarding other agents' preferences. Such uncertainties have impacts on the coalition's stability which is not reflected in the current stability criteria. / In this thesis, we extend the classic stability concept of the non-transferable utility core by proposing new belief based stability criteria under uncertainty, and illustrate how the new concept can be used to analyze the stability of a new type of belief-based coalition formation game. Mechanisms for reaching solutions of the new stable criteria is proposed and a real life application example is studied. / Chan, Chi Kong. / Adviser: Ho-Fung Leung. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3594. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 101-103). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Since the sequential inference framework determines the number of total samples in real-time based on the history data, it yields quicker decision compared to its fixed-sample-size counterpart, provided the appropriate early termination rule. This advantage is particularly appealing in the system where data is acquired in sequence, and both the decision accuracy and latency are of primary interests. Meanwhile, the Internet of Things (IoT) technology has created all types of connected devices, which can potentially enhance the inference performance by providing information diversity. For instance, smart home network deploys multiple sensors to perform the climate control, security surveillance, and personal assistance. Therefore, it has become highly desirable to pursue the solutions that can efficiently integrate the classic sequential inference methodologies into the networked multi-agent systems. In brief, this thesis investigates the sequential hypothesis testing problem in multi-agent networks, aiming to overcome the constraints of communication bandwidth, energy capacity, and network topology so that the networked system can perform sequential test cooperatively to its full potential. The multi-agent networks are generally categorized into two main types. The first one features a hierarchical structure, where the agents transmit messages based on their observations to a fusion center that performs the data fusion and sequential inference on behalf of the network. One such example is the network formed by wearable devices connected with a smartphone. The central challenges in the hierarchical network arise from the instantaneous transmission of the distributed data to the fusion center, which is constrained by the battery capacity and the communication bandwidth in practice. Therefore, the first part of this thesis is dedicated to address these two constraints for the hierarchical network. In specific, aiming to preserve the agent energy, Chapter 2 devises the optimal sequential test that selects the "most informative" agent online at each sampling step while leaving others in idle status. To overcome the communication bottleneck, Chapter 3 proposes a scheme that allows distributed agents to send only one-bit messages asynchronously to the fusion center without compromising the performance. In contrast, the second type of networks does not assume the presence of a fusion center, and each agent performs the sequential test based on its own samples together with the messages shared by its neighbours. The communication links can be represented by an undirected graph. A variety of applications conform to such a distributed structure, for instance, the social networks that connect individuals through online friendship and the vehicular network formed by connected cars. However, the distributed network is prone to sub-optimal performance since each agent can only access the information from its local neighborhood. Hence the second part of this thesis mainly focuses on optimizing the distributed performance through local message exchanges. In Chapter 4, we put forward a distributed sequential test based on consensus algorithm, where agents exchange and aggregate real-valued local statistics with neighbours at every sampling step. In order to further lower the communication overhead, Chapter 5 develops a distributed sequential test that only requires the exchange of quantized messages (i.e., integers) between agents. The cluster-based network, which is a hybrid of the hierarchical and distributed networks, is also investigated in Chapter 5.