In this thesis, besides the developing a bilateral automated negotiation model between agents, in incomplete information state, integrating the personality effects of human on the negotiation process and outcomes, we proposed an architecture of such agents ("buyer" or "seller"). To do so, a new offer generation approach of three adaptive families of tactics has been proposed as follows : the time dependent tactics (time supposed as continuous), behavior dependent tactics, and time independent tactics.This thesis takes into consideration also the personality effects (of negotiator agent) on negotiation process and outcome. In fact, with regard to "Big five" personality model and introducing the cognitive orientations, we have developed a negotiator agent's architecture based on personality. This architecture is, mainly, inspired from the game theory. In fact, the artificial agent's cognition in terms of negotiation is considered as a certain negotiator's mental orientation favorising the concession of the negotiator agent towards one of following three equilibria (based on game theory) : Win-Lose, Lose-Win, and Win-Win According to the privileged orientation and the personality of negotiator, such a negotiator agent decides the adequate combination of tactics (models, etc) in order to modulate, consequently, the expected outcomes of negotiation

Multi-agent reinforcement learning (MARL) poses the same planning problem as traditional reinforcement learning (RL): What actions over time should an agent take in order to maximize its rewards? MARL tackles a challenging set of problems that can be better understood by modeling them as having a relatively simple environment but with complex dynamics attributed to the presence of other agents who are also attempting to maximize their rewards. A great wealth of research has developed around specific subsets of this problem, most notably when the rewards for each agent are either the same or directly opposite each other. However, there has been relatively little progress made for the general problem. This thesis address this lack. Our goal is to tackle the most general, least restrictive class of MARL problems. These are general-sum, non-deterministic, infinite horizon, multi-agent sequential decision problems of complete and incomplete information. Towards this goal, we engage in two complementary endeavors: the creation of tractable models and the construction of efficient algorithms to solve these models. We tackle three well known models: stochastic games, decentralized partially observable Markov decision problems, and partially observable stochastic games. We also present a new fourth model, Markov games of incomplete information, to help solve the partially observable models. For stochastic games and decentralized partially observable Markov decision problems, we develop novel and efficient value iteration algorithms to solve for game theoretic solutions. We empirically evaluate these algorithms on a range of problems, including well known benchmarks and show that our value iteration algorithms perform better than current policy iteration algorithms. Finally, we argue that our approach is easily extendable to new models and solution concepts, thus providing a foundation for a new class of multi-agent value iteration algorithms.

The creation of expressive styles for digital art is one of the primary goals in non-photorealistic rendering. In this paper, we introduce a swarm-based multi-agent system that is capable of producing expressive imagery through the use of multiple digital images. At birth, agents in our system are assigned a digital image that represents their 'aesthetic ideal'. As agents move throughout a digital canvas they try to 'realize' their ideal by modifying the pixels in the digital canvas to be closer to the pixels in their aesthetic ideal. When groups of agents with different aesthetic ideals occupy the same canvas, a new image is created through the convergence of their competing aesthetic goals. We use our system to explore the concepts and techniques from a number of Modern Art movements and to create an interactive media installation. The simple implementation and effective results produced by our system makes a compelling argument for more research using swarm-based multi-agent systems for non-photorealistic rendering. / Graduate

The objective of this thesis is to develop decentralized methods for building robust multi-agent networks through self-organization. Multi-agent networks appear in a large number of natural and engineered systems, including but not limited to, biological networks, social networks, communication systems, transportation systems, power grids, and robotic swarms. Networked systems typically consist of numerous components that interact with each other to achieve some collaborative tasks such as flocking, coverage optimization, load balancing, or distributed estimation, to name a few. Multi-agent networks are often modeled via interaction graphs, where the nodes represent the agents and the edges denote direct interactions between the corresponding agents. Interaction graphs play a significant role in the overall behavior and performance of multi-agent networks. There- fore, graph theoretic analysis of networked systems has received a considerable amount of attention within the last decade. In many applications, network components are likely to face various functional or structural disturbances including, but not limited to, component failures, noise, or malicious attacks. Hence, a desirable network property is robustness, which is the ability to perform reasonably well even when the network is subjected to such perturbations. In this thesis, robustness in multi-agent networks is pursued in two parts. The first part presents a decentralized graph reconfiguration scheme for formation of robust interaction graphs. Particularly, the proposed scheme transforms any interaction graph into a random regular graph, which is robust to the perturbations of their nodes/links. The second part presents a decentralized coverage control scheme for optimal protection of networks by some mobile security resources. As such, the proposed scheme drives a group of arbitrarily deployed resources to optimal locations on a network in a decentralized fashion.

In a multi-agent system (MAS), the overall performance is greatly influenced by both the design and the control of the agents. The physical design determines the agent capabilities, and the control strategies drive the agents to pursue their objectives using the available capabilities. The objective of this thesis is to incorporate control strategies in the early conceptual design of an MAS. As such, this thesis proposes a methodology that mainly explores the interdependency between the design variables of the agents and the control strategies used by the agents. The output of the proposed methodology, i.e. the interdependency between the design variables and the control strategies, can be utilized in the requirement analysis as well as in the later design stages to optimize the overall system through some higher fidelity analyses. In this thesis, the proposed methodology is applied to a persistent multi-UAV surveillance problem, whose objective is to increase the situational awareness of a base that receives some instantaneous monitoring information from a group of UAVs. Each UAV has a limited energy capacity and a limited communication range. Accordingly, the connectivity of the communication network becomes essential for the information flow from the UAVs to the base. In long-run missions, the UAVs need to return to the base for refueling with certain frequencies depending on their endurance. Whenever a UAV leaves the surveillance area, the remaining UAVs may need relocation to mitigate the impact of its absence. In the control part of this thesis, a set of energy-aware control strategies are developed for efficient multi-UAV surveillance operations. To this end, this thesis first proposes a decentralized strategy to recover the connectivity of the communication network. Second, it presents two return policies for UAVs to achieve energy-aware persistent surveillance. In the design part of this thesis, a design space exploration is performed to investigate the overall performance by varying a set of design variables and the candidate control strategies. Overall, it is shown that a control strategy used by an MAS affects the influence of the design variables on the mission performance. Furthermore, the proposed methodology identifies the preferable pairs of design variables and control strategies through low fidelity analysis in the early design stages.

Energy is consumed every day at home as we perform simple tasks, such as watching television, washing dishes and heating/cooling home spaces during season of extreme weather conditions, using appliances, or turning on lights. Most often, the energy resources used in residential systems are obtained from natural gas, coal and oil. Moreover, climate change has increased awareness of a need for expendable, energy resources. As a result, carbon dioxide emissions are increasing and creating a negative effect on our environment and on our health. In fact, growing energy demands and limited natural resource might have negative impacts on our future. Therefore, saving energy is becoming an important issue in our society and it is receiving more attention from the research community. This thesis introduces a intelligent energy controller algorithm based on software agent approach that reduce the energy consumption at home for both heating and cooling spaces by considering the user’s occupancy, outdoor temperature and user’s preferences as input to the system. Thus the proposed approach takes into consideration the occupant’s preferred temperature, the occupied and unoccupied spaces, as well as the time spent in each area of the home. A Java based simulator has been implemented to simulate the algorithm for saving energy in heating and cooling systems. The results from the simulator are compared to the results of using HOT2000, which is Canada’s leading residential energy analysis and rating software developed by CanmetENERGY’s Housing, Buildings, Communities and Simulation (HBCS) group. We have calculated how much energy a home modelled will use under emulated conditions. The results showed that the implementation of the proposed energy controller algorithm can save up to 50% in energy consumption in homes dedicated to heating and cooling systems compared to the results obtained by using HOT2000.

This thesis relates to the usage of trust modelling in multi-agent systems - environments in which there are interacting software agents representing various users (for example, buyers and sellers exchanging products and services in an electronic marketplace). In such applications, trust modelling may be crucial to allow one group of agents (in the e-commerce scenario, buyers) to make effective decisions about which other agents (i.e., sellers) are the most appropriate partners. A number of existing multi-agent trust models have been proposed in the literature to help buyers accurately select the most trustworthy sellers. Our contribution is to propose several modifications that can be applied to existing probabilistic multi-agent trust models. First, we examine how the accuracy of the model can be improved by limiting the network to a portion of the population consisting of the most trustworthy agents, such that the less trustworthy contributions of the remaining agents can be ignored. In particular, we explore how this can be accomplished by either setting a maximum size for a buyer's advisor network or setting a minimum trustworthiness threshold for agents to be accepted into that advisor network, and develop methods for appropriately selecting the values to limit the network size. We demonstrate that for two models, both the Personalized Trust Model (PTM) developed by Zhang as well as TRAVOS, these approaches will yield significant improvements to the accuracy of the trust model, as opposed to using an unrestricted advisor network. Our final proposed modification is to use an advisor referral system in combination with one of the network-limiting approaches. This would ensure that if a particular agent within the advisor network had not met a specified level of experience with the seller under consideration, it could be replaced by another agent that had greater experience with that seller, which should in turn allow for a more accurate modelling of the seller's trustworthiness. We present a particular approach for replacing advisors, and show that this will yield additional improvements in trust-modelling accuracy with both PTM and TRAVOS, especially if the limiting step were such that it would yield a very small advisor network. We believe that these techniques will be very useful for trust researchers seeking to improve the accuracy of their own trust models, and to that end we explain how other researchers could apply these modifications themselves, in order to identify the optimal parameters for their usage. We discuss as well the value of our proposals for identifying an "optimal" size for a social network, and the use of referral systems, for researchers in other areas of artificial intelligence.

In this thesis, we discuss models for multi-agent dynamical systems. We study the tracking/migration problem for flocks and a theoretical framework for design and analysis of flocking algorithm is presented. The interactions between agents in the systems are denoted by potential functions that act as distance functions, hence, the design of proper potential functions are crucial in modelling and analyzing the flocking problem for multi-agent dynamical systems. Constructions for both non-smooth potential functions and smooth potential functions with finite cut-off are investigated in detail. The main contributions of this thesis are to extend the literature of continuous flocking models with impulsive control and delay. Lyapunov function techniques and techniques for stability of continuous and impulsive switching system are used, we study the asymptotic stability of the equilibrium of our models with impulsive control and discovery that by applying impulsive control to Olfati-Saber's continuous model, we can remove the damping term and improve the performance by avoiding the deficiency caused by time delay in velocity sensing. Additionally, we discuss both free-flocking and constrained-flocking algorithm for multi-agent dynamical system, we extend literature results by applying velocity feedbacks which are given by the dynamical obstacles in the environment to our impulsive control and successfully lead to flocking with obstacle avoidance capability in a more energy-efficient way. Simulations are given to support our results, some conclusions are made and future directions are given.

Planning framework is commonly used to represent intelligent agents effectively and to model complex behavior. In planning framework, resource-based perspective is interesting in the sense that in a multi-agent environment, exchange of resources can form a cooperative interaction. In resource based plan coordination, each agent constructs an individual plan, then plans are examined by a central plan revision unit for possibilities of removing actions. Domain of this work is the classical postmen domain that is also modifed to have non-sub-additive property.The domain is has numerous challenges that is not considered in the original plan coordination model. Moreover, the plan coordination algorithm is used in the re-planning phase, as the environment changes through plan execution.These issues are common for the realistic environments and the details of the original plan coordination perspective are renewed to cope with these issues.

In this work, reinforcement learning algorithms are studied with the help of potential field methods, using robosoccer simulators as test beds. Reinforcement Learning (RL) is a framework for general problem solving where an agent can learn through experience. The soccer game is selected as the problem domain a way of experimenting multi-agent team behaviors because of its popularity and complexity.