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

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

How to Do Things With Words

The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents. Agent architectures can be thought of as software engineering models of agents;researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the properties specified by agent theorists. Finally, agent languages are software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper is not intended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.

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Intelligent Agents: Theory and Practice

The present historical moment may seem a particularly inopportune time to review Bowling Alone, Robert Putnam's latest exploration of civic decline in America. After all, the outpouring of volunteerism, solidarity, patriotism, and self-sacrifice displayed by Americans in the wake of the September 11 terrorist attacks appears to fly in the face of Putnam's central argument: that \"social capital\" -defined as \"social networks and the norms of reciprocity and trustworthiness that arise from them\" (p. 19)'has declined to dangerously low levels in America over the last three decades. However, Putnam is not fazed in the least by the recent effusion of solidarity. Quite the contrary, he sees in it the potential to \"reverse what has been a 30to 40-year steady decline in most measures of connectedness or community.\"' As an example of how the current \"war on terrorism\" could generate a durable civic renewal, Putnam points to the burst in civic practices that occurred during and after World War II, which he says \"permanently marked\" the generation that lived through it and had a \"terrific effect on American public life over the last half-century.\" 3 If Americans can follow this example and channel their current civic

Bowling alone: The collapse and revival of American community

The main objective of this work is to develop a multi-modal logic of Intention and Attempt. We call this logic LIA. All formal results are focused on the notion of attempt. We substitute the dynamic molecular notion action by his atomic constituent attempt and define the former from the latter. The relations between attempts, goals, beliefs and present-directed intentions are studied. A section of the paper is devoted to the analysis of the relations of our modal logic with a situation calculus-style approach.

Introducing Attempt in a modal logic of intentional action

Last years have seen the raise of several contexts such as Ambient Intelligence, Augmented Reality where Artificial Intelligence is combined with other domains such as Ubiquitous Computing, Sensor Network Technologies in order to provide proactive and responsive services to users. However, these systems are most of the times ad hoc, lacking a conceptual foundation. In this paper we provide a broad overview of mirror worlds, as physically situated agent societies, useful in particular as a framework for investigating inter-disciplinary aspects --- from cognition to interaction, cooperation, governance --- concerning future smart environments and cities shaped as large-scale mixed-reality systems.

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Mirror Worlds as Agent Societies Situated in Mixed Reality Environments

Multi-agent simulation as a tool for modeling societies: Application to social differentiation in ant colonies.- Experiments in multi-agent system dynamics.- Social aggregations in evolving neural networks.- An architecture for action, emotion, and social behavior.- Game theory vs. multiple agents: The iterated prisoner's dilemma.- Trust in distributed artificial intelligence.- Negotiation with incomplete information about worth: Strict versus tolerant mechanisms.- Equilibratory approach to distributed resource allocation: Toward coordinated balancing.- Strategic interaction in oligopolistic markets - experimenting with real and artificial agents.- Multi agent coordinated decision-making using epistemic utility theory.- The search for coordination: Knowledge-guided abstraction and search in a hierarchical behavior space.- Multi-agent planning as search for a consensus that maximizes social welfare.- Planned team activity.- Some requirements for mobile distributed telecomputing architecture.- Multi-agent research in the knobotics group.- A conflict resolution-based decentralized multi-agent problem solving model.- Decision coordination in production management.- MAKILA: A tool for the development of cooperative societies.- User role in problem solving with distributed artificial intelligent systems.

Artificial Social Systems: 4th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, MAAMAW '92, S. Martino al Cimino, Italy, July 29 - 31, 1992. Selected Papers

Meta-emotions and the complexity of human emotional experience

This paper presents a systematic analysis of the various steps of goal-processing and intention creation, as the final outcome of goal-driven action generation. Intention theory has to be founded on goal theory: intentions require means-end reasoning and planning, conflict resolution, coherence. The process of intention formation and intentional action execution is strictly based on specific sets of beliefs (predictions, evaluations, calculation of costs, responsibility beliefs, competence, etc.). The origin of an intention is not necessarily a “desire” (which is just a kind of goal). Intention is a two-layered goal-structure: the intended action(s) to be executed, and the intended outcome motivating that action—with two distinct kinds of “failure”. This belief-goal perspective also allows to examine two stages/types of intention, and the relations and differences between intention “in agenda” (future directed) and intention under execution (intention in action). I will argue that the will is much more than the intention driving an intentional action. I will also claim that intentions are not there just for motivating and regulating intentional actions (from the motor level to more complex behaviors), but that they play also several other important roles.

Cristiano Castelfranchi

Papers

Introducing Attempt in a modal logic of intentional action

Mirror Worlds as Agent Societies Situated in Mixed Reality Environments

Artificial Social Systems: 4th European Workshop on Modelling Autonomous Agents in a Multi-Agent World, MAAMAW '92, S. Martino al Cimino, Italy, July 29 - 31, 1992. Selected Papers

Meta-emotions and the complexity of human emotional experience

Intentions in the Light of Goals