Decision theoretic agent design for personal rapid transit systems

This paper details a learning decision-theoretic intelligent agent designed to solve the problem of guiding vehicles in the context of Personal Rapid Transit (PRT). The intelligent agents are designed using Bayesian Decision Networks. The agents are designed to utilize the known methods of machine learning with Bayesian Networks (BN): parameter learning and structure learning. In addition, a new method of machine learning with BNs, termed utility learning in this paper, is introduced. BN software for Matlab is used to realize the proposed agent. Additional software is written to simulate the PRT problem using various intelligent agents that utilize one or more learning methods

Online Structure, Parameter, and Utility Updating of Bayesian Decision Networks for Cooperative Decision-Theoretic Agents

Multi-agent systems, systems consisting of more than one acting and decision making entities, are of great interest to researchers because they have advantages for some specific tasks where it would be more effective to use multiple small and simple robots rather than a large and complex one. One of the major problems with multi-agent systems is developing a means to organize or control the overall behavior of the system. Typically, multi-agent control involves one of two structures. In some designs, there is a hierarchy with some robots being leaders and other followers. Other designs involve robot specialization towards one particular task or individual robots which loosely or strongly cooperate in some manner to yield the desired behavior. This thesis studies using bayesian decision networks (BDNs) as a method to control individual robots to achieve some group or cooperative behavior. BDNs are powerful tools enabling designers of intelligent agents to model the agent\u27s environment and the behavior of other agents without expert knowledge about a system. The probabilistic nature of these networks allows agents to learn about themselves and their environment by updating their bayesian network (BN) with new observations. While two methods of learning and responding to change in the environment with BNs, parameter learning and structure learning, have been studied by many researchers as a means to control a single robot or teams of robots, a third method, utility updating, has seen little study. This work is thus a novel study of BN control since it incorporates all three methods to develop a decision theoretic agent (DTA). The agent is applied to a modified version of a personal rapid transit (PRT) problem (or personal automated transport (PAT)) that is simulated in Matlab. PRT is a proposed public transport method which offers automated on-demand transportation between any two nodes of the transportation network. The PRT problem of interest is that of autonomous control. This can be likened to one of multi-agent control of many identical agents. Several agents are developed to solve the problem, a rule based agent and BN-agents which use various subsets of the three network updating methods. The experimental results show that the DTA that uses parameter, structure, and utility updating could be a superior solution to agents based only on some subset of those methods

Changing Games and Evolving Contexts: Political Bargaining In European Energy Disputes

Energy has recently become a very important item on the political agenda of most Western countries; it is bound to be even more so in the future, due to the increasing scarcity of natural gas and oil. With Russia using the energy weapons to advance its economic and political goals, energy security has become a central topic in European politics. Important political bargaining models like game theory can offer valuable insights and contribute to the explanation of the outcome of important political confrontations like the ones between Russia and its former satellites. Game theory, however, fails to adequately account for an evolving context which can affect the preferences of the disputing actors, an issue which is likely to systematically produce inaccurate explanations and predictions. The relevance of the preferences of external actors will be demonstrated in this work by finding empirical evidence that the start of the dispute has damaged GDP and stock market performances of external players; in fact, this would give them a reason to become a relevant part of the game by exerting pressure on the two players so as to reach an agreement in the shortest time possible. The growing importance of external actors, I argue, needs to be modeled by game theory because, as it was especially the case for the Russia-Ukraine dispute in 2006, their role can be pivotal to in determining the duration and the outcome of the political bargaining. I select a Pooled Panel Nonlinear Auto Regressive Conditional Heteroskedasticity (PP-NARCH) model and Box-Tiao intervention models to support the validity of what I define a Fully-Fuzzy game. I rely more on the general message conveyed by the statistical models considered, thus freeing my analysis from the specificity of the model chosen for its better fit. My work, however, would be incomplete if simply finding empirical evidence of a negative effect of the gas disputes on the real growth of GDP and main stock markets of European countries. I summarize the most relevant statements, agreements, and partnerships which are likely to have exerted pressure on Russia and the other negotiating country