Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

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Multiagent systems: a modern approach to distributed artificial intelligence

The agent computing paradigm is rapidly emerging as one of the powerful technologies for the development of large-scale distributed systems to deal with the uncertainty in a dynamic environment. The domain of traffic and transportation systems is well suited for an agent-based approach because transportation systems are usually geographically distributed in dynamic changing environments. Our literature survey shows that the techniques and methods resulting from the field of agent and multiagent systems have been applied to many aspects of traffic and transportation systems, including modeling and simulation, dynamic routing and congestion management, and intelligent traffic control. This paper examines an agent-based approach and its applications in different modes of transportation, including roadway, railway, and air transportation. This paper also addresses some critical issues in developing agent-based traffic control and management systems, such as interoperability, flexibility, and extendibility. Finally, several future research directions toward the successful deployment of agent technology in traffic and transportation systems are discussed.

A Review of the Applications of Agent Technology in Traffic and Transportation Systems

Preface (R.J. Aumann, S. Hart). Strategic equilibrium (E. van Damme). Foundations of strategic equilibrium (J. Hillas, E. Kohlberg). Incomplete information (R.J. Aumann, A. Heifetz). Non-zero-sum two-person games (T.E.S. Raghavan). Computing equilibria for two-person games (B. von Stengel). Non-cooperative games with many players (M. Ali Khan, Y. Sun). Stochastic games (J-F. Mertens). Stochastic games: recent results (N. Vieille). Game theory and industrial organization (K. Bagwell, A. Wolinsky). Bargaining with incomplete information (L.M. Ausubel, P. Cramton, R.J. Deneckere). Inspection Games (R. Avenhaus, B.V. Stengel, S.Zamir). Economic history and game theory (A. Greif). The shapley value (E. Winter). Variations on the shapley value (D. Monderer, D. Samet). Values of non-transferable utility games (R. McLean). Values of games with infinitely many players (A. Neyman). Values of perfectly competitive economies (S. Hart). Some other economic applications of the value (J-F. Mertens). Strategic aspects of political systems (J. Banks). Game-theoretic analysis of legal rules and institutions (J-P. Benoit, L.A. Kornhauser). Implementation Theory (T. Palfrey). Game Theory and experimental Gaming (M. Shubik).

Handbook of game theory with economic applications

An ontology of slums for image - based classification

Modern decision support systems (DSS) not only store large amounts of decision-relevant data, but also aim at assisting decision-makers to explore the meaning of that data, and to take decisions based on understanding. In transportation domains, a multiagent approach to the construction of DSS is becoming increasingly popular, because it does not only reduce design complexity, but it also adequately supports a dialogue-based stance on decision support interactions. However, despite recent advances in the field of agent-oriented software engineering, a principled approach to the design of multiagent systems for decision support is still to come. In this paper, we outline a design method for the construction of agent-based DSS. Setting out from an organisational and communicative model of decision support environments, we present an abstract architecture for multiagent DSS. We then show how this architecture is instantiated for real-world problems by means of two prototypes for transportation management.

http://www.biblioteca.uma.es/bbldoc/tesisuma/16611329.pdf

Decision support for traffic management based on organisational and communicative multiagent abstractions

This paper reports on the design and implementation of a multi-agent decision support system for the bus fleet management domain. In particular, we show a complete description of the proposed multi-agent architecture and focus mainly on knowledge and software engineering features. The system has been developed in collaboration with the Malaga urban bus company (EMT) and it is based on a faithful reproduction of the real operating conditions of three existing lines of this company.

Ontologies and agents for a bus fleet management system

This article describes how agent and knowledge technology can be used to build advanced software systems that support operational decision making in complex domains. In particular, we present an abstract architecture and design guidelines for agent-based decision support systems. We illustrate our approach with a case study in the transportation management domain.

http://www.biblioteca.uma.es/bbldoc/articulos/16493758.pdf

Multi-agent systems for decision support: a case study in the transportation management domain

In the last few years the use of coalition formation algorithms in multi-agent systems has been proposed as a possible way of modelling autonomous agent cooperation. Game theory provides different concepts for the stability of solutions in cooperative games, regarding the fairness of the resultant payment configuration. One of these is the core. In this paper we present an analysis -based upon game theory- for a class of task-oriented problems arising from some Internet transactions. A procedure that guarantees an optimum task allocation is defined, and a new payoff division model is proposed for the corresponding value. We prove that the proposed payoff division lies inside the core. The whole computation (of the optimal value and the payoffs) has a polynomial complexity in the number of agents.

A stable and feasible payoff division for coalition formation in a class of task oriented domains

This paper presents an abstract multiagent architecture useful for Decision Support Systems (DSS), and shows how this architecture can be instantiated to a particular real-world domain, Bus Fleet Management (BFM) After discussing the benefits of adopting a multiagent approach for DSS, the paper introduces the real world case study and analyzes it Next, an abstract MAS architecture for DSS in general, and specialized then for BFM case study is presented The architecture includes a set of different agents which are in charge of different social roles, and what is needed for their coordination, the Coordination Facilitator and the coordination protocols The paper is concentrated in this point showing a concrete example in the BFM domain

Francisco Triguero

Papers

Decision support for traffic management based on organisational and communicative multiagent abstractions

Ontologies and agents for a bus fleet management system

Multi-agent systems for decision support: a case study in the transportation management domain

A stable and feasible payoff division for coalition formation in a class of task oriented domains

Agent coordination for bus fleet management