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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
101

Self-Reliance Guidelines for Large Scale Robot Colonies

Engwirda, Anthony, N/A January 2007 (has links)
A Large Scale Robot Colony (LSRC) is a complex artifact comprising of a significant population of both mobile and static robots. LSRC research is in its literary infancy and it is therefore necessary to rely upon external fields for the appropriate framework, Multi Agent Systems (MAS) and Large Scale Systems (LSS). At the intersection of MAS, LSS and LSRC exist near identical issues, problems and solutions. If attention is paid to coherence then solution portability is possible. The issue of Self-Reliability is poorly addressed by the MAS research field. Disparity between the real world and simulation is another area of concern. Despite these deficiencies, MAS and LSS are perceived as the most appropriate frameworks. MAS research focuses on three prime areas, cognitive science, management and interaction. LSRC is focused on Self-Sustainability, Self-Management and Self-Organization. While LSS research was not primarily intended for populations of mobile robots, it does address key issues of LSRC, such as effective sustainability and management. Implementation of LSRC that is based upon the optimal solution for any one or two of the three aspects will be inferior to a coherent solution based upon all three. LSRC’s are complex organizations with significant populations of both static and mobile robots. The increase in population size and the requirement to address the issue of Self-Reliance give rise to new issues. It is no longer sufficient to speak only in terms of robot intelligence, architecture, interaction or team behaviour, even though these are still valid topics. Issues such as population sustainability and management have greater significance within LSRC. As the size of a robot populations increases, minor uneconomical decisions and actions inhibit the performance of the population. Interaction must be made economical within the context of the LSRC. Sustainability of the population becomes significant as it enables stable performance and extended operational lifespan. Management becomes significant as a mechanism to direct the population so as to achieve near optimal performance. The Self-Sustainability, Self-Management and Self-Organization of LSRC are vastly more complex than in team robotics. Performance of the overall population becomes more significant than individual or team achievement. This thesis is a presentation of the Cooperative Autonomous Robot Colony (CARC) architecture. The CARC architecture is novel in that it offers a coherent baseline solution to the issue of mobile robot Self-Reliance. This research uses decomposition as a mechanism to reduce problem complexity. Self-Reliance is decomposed into Self-Sustainability, Self-Management, and Self-Organization. A solution to the issue of Self-Reliance will comprise of conflicting sub-solutions. A product of this research is a set of guidelines that manages the conflict of sub-solutions and maintains a coherent solution. In addressing the issue of Self-Reliance, it became apparent that Economies of Scale, played an important role. The effects of Economies of Scale directed the research towards LSRC’s. LSRC’s demonstrated improved efficiency and greater capability to achieve the requirements of Self-Reliance. LSRC’s implemented with the CARC architecture would extend human capability, enabling large scale operations to be performed in an economical manner, within real world and real time environments, including those of a remote and hostile nature. The theory and architecture are supported using published literature, experiments, observations and mathematical projections. Contributions of this work are focused upon the three pillars of Self-Reliance addressed by CARC: Self-Sustainability, Self-Management and Self-Organization. The chapter on Self-Sustainability explains and justifies the relevance of this issue, what it is, why it is important and how it can be achieved. Self-Sustainability enables robots to continue to operate beyond disabling events by addressing failure and routine maintenance. Mathematical projections are used to compare populations of non-sustained and sustained robots. Computer modeling experiments are used to demonstrate the feasibility of Self-Sustainability, including extended operational life, the maintenance of optimal work flow and graceful physical degradation (GPD). A detailed explanation is presented of Sustainability Functions, Colony Sites, Static Robot Roles, Static Robot Failure Options, and Polymorphism. The chapter on Self-Management explores LSS research as a mechanism to exert influence over a LSRC. An experimental reactive management strategy is demonstrated. This strategy while limited does indicate promising potential directions for future research including the Man in the Loop (MITL) strategy highly desired by NASA JPL for off world command and control of a significant robot colony (Huntsberger, et. al., 2000). Experiments on Communication evaluate both Broadcast Conveyance (BC) and Message Passing Conveyance (MPC). These experiments demonstrate the potential of Message Passing as a low cost system for LSRC communication. Analysis of Metrics indicates that a Performance Based Feedback Method (PBFM) and a Task Achievement Method (TAM) are both necessary and sufficient to monitor a LSRC. The chapter on Self-Organization describes a number of experiments, algorithms and protocols on Reasoning Robotics, a minor variant of Reactive Robotics. Reasoning Robotics utilizes an Event Driven Architecture (EDA) rather than a Stimulus Driven Architecture (SDA) common to Reactive Robotics. Enhanced robot performance is demonstrated by a combination of EDA and environmental modification enabling stigmergy. These experiments cover Intersection Navigation with contingency for Multilane Intersections, a Radio Packet Controller (RPC) algorithm, Active and Passive Beacons including a communication protocol, mobile robot navigation using Migration Decision Functions (MDF’s), including MDF positional errors. The central issue addressed by this thesis is the production of Self-Reliance guidelines for LSRC’s. Self-Reliance is perceived as a critical issue in advancing the useful and productive applications for LSRC’s. LSRC’s are complex with many issues in related fields of MAS and LSS. Decomposition of Self-Reliance into Self-Sustainability, Self-Management and Self-Organization were used to aid in problem understanding. It was found that Self-Sustainability extends the operational life of individual robots and the LSRC. Self-Management enables the exertion of human influence over the LSRC, such that the ratio of humans to robots is reduced but not eliminated. Self-Organization achieves and enhances performance through a routine and reliable LSRC environment. The product of this research was the novel CARC architecture, which consists of a set of Self-Reliance guidelines and algorithms. The Self-Reliance guidelines manage conflict between optimal solutions and provide a framework for LSRC design. This research was supported by literature, experiments, observations and mathematical projections.
102

Interaction and Intelligent Behavior

Mataric, Maja J. 01 August 1994 (has links)
We introduce basic behaviors as primitives for control and learning in situated, embodied agents interacting in complex domains. We propose methods for selecting, formally specifying, algorithmically implementing, empirically evaluating, and combining behaviors from a basic set. We also introduce a general methodology for automatically constructing higher--level behaviors by learning to select from this set. Based on a formulation of reinforcement learning using conditions, behaviors, and shaped reinforcement, out approach makes behavior selection learnable in noisy, uncertain environments with stochastic dynamics. All described ideas are validated with groups of up to 20 mobile robots performing safe--wandering, following, aggregation, dispersion, homing, flocking, foraging, and learning to forage.
103

An Architecture For Multi-Agent Systems Operating In Soft Real-Time Environments With Unexpected Events

Micacchi, Christopher January 2004 (has links)
In this thesis, we explore the topic of designing an architecture and processing algorithms for a multi-agent system, where agents need to address potential unexpected events in the environment, operating under soft real-time constraints. We first develop a classification of unexpected events into Opportunities, Barriers and Potential Causes of Failure, and outline the interaction required to support the allocation of tasks for these events. We then propose a hybrid architecture to provide for agent autonomy in the system, employing a central coordinating agent. Certain agents in the community operate autonomously, while others remain under the control of the coordinating agent. The coordinator is able to determine which agents should form teams to address unexpected events in a timely manner, and to oversee those agents as they perform their tasks. The proposed architecture avoids the overhead of negotiation amongst agent teams for the assignment of tasks, a benefit when operating under limited time and resource constraints. It also avoids the bottleneck of having one coordinating agent making all decisions before work can proceed in the community, by allowing some agents to work independently. We illustrate the potential usefulness of the framework by describing an implementation of a simulator loosely based on that used for the RoboCup Rescue Simulation League contest. The implementation provides a set of simulated computers, each running a simple soft real-time operating system. On top of this basic simulation we implement the model described above and test it against two different search-and-rescue scenarios. From our experiments, we observe that our architecture is able to operate in dynamic and real-time environments, and can handle, in an appropriate and timely manner, any unexpected events that occur. We also comment on the value of our proposed approach for designing adjustable autonomy multi-agent systems and for specific environments such as robotics, where reducing the overall level of communication within the system is crucial.
104

Bifurcation routes to volatility clustering

Gaunersdorfer, Andrea, Hommes, Cars H., Wagener, Florian O. O. January 2000 (has links) (PDF)
A simple asset pricing model with two types of adaptively learning traders, fundamentalists and technical analysts, is studied. Fractions of these trader types, which are both boundedly rational, change over time according to evolutionary learning, with technical analysts conditioning their forecasting rule upon deviations from a benchmark fundamental. Volatility clustering arises endogenously in this model. Two mechanisms are proposed as an explanation. The first is coexistence of a stable steady state and a stable limit cycle, which arise as a consequence of a so-called Chenciner bifurcation of the system. The second is intermittency and associated bifurcation routes to strange attractors. Both phenomena are persistent and occur generically in nonlinear multi-agent evolutionary systems. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
105

An Architecture For Multi-Agent Systems Operating In Soft Real-Time Environments With Unexpected Events

Micacchi, Christopher January 2004 (has links)
In this thesis, we explore the topic of designing an architecture and processing algorithms for a multi-agent system, where agents need to address potential unexpected events in the environment, operating under soft real-time constraints. We first develop a classification of unexpected events into Opportunities, Barriers and Potential Causes of Failure, and outline the interaction required to support the allocation of tasks for these events. We then propose a hybrid architecture to provide for agent autonomy in the system, employing a central coordinating agent. Certain agents in the community operate autonomously, while others remain under the control of the coordinating agent. The coordinator is able to determine which agents should form teams to address unexpected events in a timely manner, and to oversee those agents as they perform their tasks. The proposed architecture avoids the overhead of negotiation amongst agent teams for the assignment of tasks, a benefit when operating under limited time and resource constraints. It also avoids the bottleneck of having one coordinating agent making all decisions before work can proceed in the community, by allowing some agents to work independently. We illustrate the potential usefulness of the framework by describing an implementation of a simulator loosely based on that used for the RoboCup Rescue Simulation League contest. The implementation provides a set of simulated computers, each running a simple soft real-time operating system. On top of this basic simulation we implement the model described above and test it against two different search-and-rescue scenarios. From our experiments, we observe that our architecture is able to operate in dynamic and real-time environments, and can handle, in an appropriate and timely manner, any unexpected events that occur. We also comment on the value of our proposed approach for designing adjustable autonomy multi-agent systems and for specific environments such as robotics, where reducing the overall level of communication within the system is crucial.
106

A Framework for Coordinated Control of Multi-Agent Systems

Li, Howard January 2006 (has links)
Multi-agent systems represent a group of agents that cooperate to solve common tasks in a dynamic environment. Multi-agent control systems have been widely studied in the past few years. The control of multi-agent systems relates to synthesizing control schemes for systems which are inherently distributed and composed of multiple interacting entities. Because of the wide applications of multi-agent theories in large and complex control systems, it is necessary to develop a framework to simplify the process of developing control schemes for multi-agent systems. <br /><br /> In this study, a framework is proposed for the distributed control and coordination of multi-agent systems. In the proposed framework, the control of multi-agent systems is regarded as achieving decentralized control and coordination of agents. Each agent is modeled as a Coordinated Hybrid Agent (CHA) which is composed of an intelligent coordination layer and a hybrid control layer. The intelligent coordination layer takes the coordination input, plant input and workspace input. After processing the coordination primitives, the intelligent coordination layer outputs the desired action to the hybrid layer. In the proposed framework, we describe the coordination mechanism in a domain-independent way, as simple abstract primitives in a coordination rule base for certain dependency relationships between the activities of different agents. The intelligent coordination layer deals with the planning, coordination, decision-making and computation of the agent. The hybrid control layer of the proposed framework takes the output of the intelligent coordination layer and generates discrete and continuous control signals to control the overall process. In order to verify the feasibility of the proposed framework, experiments for both heterogeneous and homogeneous Multi-Agent Systems (MASs) are implemented. In addition, the stability of systems modeled using the proposed framework is also analyzed. The conditions for asymptotic stability and exponential stability of a CHA system are given. <br /><br /> In order to optimize a Multi-Agent System (MAS), a hybrid approach is proposed to address the optimization problem for a MAS modeled using the CHA framework. Both the event-driven dynamics and time-driven dynamics are included for the formulation of the optimization problem. A generic formula is given for the optimization of the framework. A direct identification algorithm is also discussed to solve the optimization problem.
107

Advisor Networks and Referrals for Improved Trust Modelling in Multi-Agent Systems

Gorner, Joshua Mark January 2011 (has links)
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.
108

Formation Preserving Navigation Of Agent Teams In 3-d Terrains

Bayrak, Ali Galip 01 August 2008 (has links) (PDF)
Navigation of a group of autonomous agents that are needed to maintain a formation is a challenging task which has not been studied much in especially 3-D terrains. This thesis presents a novel approach to collision free path finding of multiple agents preserving a predefined formation in a 3-D terrain. The proposed method could be used in many areas like navigation of semi-automated forces (SAF) at unit level in military simulations and non player characters (NPC) in computer games. The proposed path finding algorithm first computes an optimal path from an initial point to a target point after analyzing the 3-D terrain data from which it constructs a weighted graph. Then, it employs a real-time path finding algorithm specifically designed to realize the navigation of the group from one way point to the successive one on the optimal path generated at the previous stage, preserving the formation and avoiding collision both. A software was developed to test the methods discussed here.
109

Development Of A Multi Agent System For Negotiation Of Cost Overrun In International Construction Projects

Karakas, Kivanc 01 May 2010 (has links) (PDF)
Multiagent systems (MAS) are systems consisting of several autonomous entities, called agents, which interact with each other to either further their own interests (competition) or in pursuit of a joint goal (cooperation). 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. The aim of this thesis is to develop a multiagent system that simulates the negotiation process between parties about sharing of cost overrun in international construction projects. The developed tool can be used to understand how the risks and associated costs are shared between parties under different scenarios related with the risk allocation clauses in the contract, objectives of parties and level of knowledge about actual sources of cost overrun. MAS can be utilized by decision-makers to predict potential outcomes of a negotiation process.
110

Multiresolution Formation Preserving Path Planning In 3-d Virtual Environments

Hosgor, Can 01 September 2011 (has links) (PDF)
The complexity of the path finding and navigation problem increases when multiple agents are involved and these agents have to maintain a predefined formation while moving on a 3-D terrain. In this thesis, a novel approach for multiresolution formation representation is proposed, that allows hierarchical formations of arbitrary depth to be defined using different referencing schemes. This formation representation approach is then utilized to find and realize a collision free optimal path from an initial location to a goal location on a 3-D terrain, while preserving the formation. The proposed metod first employs a terrain analysis technique that constructs a weighted search graph from height-map data. The graph is used by an off-line search algorithm to find the shortest path. The path is realized by an on-line planner, which guides the formation along the path while avoiding collisions and maintaining the formation. The methods proposed here are easily adaptable to several application areas, especially to real time strategy games and military simulations.

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