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 focus of this work is the complex and dialectic relationship between personal, social, and institutional powers. This distinction is not new, it is even obvious or commonsensical. However, first, this multi-layered nature of power is not acknowledged in all the disciplines (for instance sociology typically rejects the idea of a connection between the personal powers and the social ones); second, in our view there is not a good analytic theory of the personal and interpersonal layers of power and of their – not so obvious – relationships with the power that we prefer to call “institutional” (where the “legal” one is just a sub-case based not simply on conventions, traditions, and social norms but on a system of laws). For example, the term “physical power” is a bit reductive, since in fact one not only needs strength, skills, and a working body, but the appropriate action plan and the relevant information, i.e. knowledge, and also the required mental abilities and conditions (motives, awareness, confidence, and so on).

The Micro-Macro Constitution of Power

The aim of this paper is to show how relevant is a trust model based on beliefs and their credibility.The approaches to the study of trust are various and very different from each of other. In our view, just a socio-cognitive approach to trust would be able to analyse the sub-components on which the final decision to trust or not is taken. In this paper we show an implementation of our socio-cognitive model of trust developed using the so-called Fuzzy Cognitive Maps. The model allows to distinguish between internal and external attributions and it introduced a degree of trust derived from the credibility of the trust beliefs, while the credibility of the beliefs derives from their sources and the sources' number, convergence, reliability (i.e. trust).With this implementation we show how the different components may change and how their impact can change depending on the specific situation and from the agent heuristics or personality. In particular, we analyse the different nature of the belief sources and their trustworthiness. We assumed different types of belief sources. For each trustier's belief one should consider what the content of the belief is, who/what the source is, how this source evaluates the belief, how the trustier evaluates this source (with respect to this belief). In addition for considering the contribution of different sources we need a theory of how they combine. The interesting thing in this paper is that starting from finding the sources of trust we are obliged to consider the trustworthiness of these sources.

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Trust in information sources as a source for trust: a fuzzy approach

The approaches to the trust study are various and very different each of other. In our view, just a socio-cognitive approach to trust would be able to analyze the sub-components (the basic beliefs) on which the final decision to trust or not is taken. In this paper we show a first implementation and advance of the socio-cognitive model of trust developed in [1, 2] using the so-called Fuzzy Cognitive Maps. With this implementation we show how the different components may change and how their impact can change depending from the specific situation and from the agent personality.

A fuzzy approach to a belief-based trust computation

We develop a conceptual and formal clarification of notion of surprise as a belief-based phenomenon by exploring a rich typology. Each kind of surprise is associated with a particular phase of cognitive processing and involves particular kinds of epistemic representations (representations and expectations under scrutiny, implicit beliefs, presuppositions). We define two main kinds of surprise: mismatch-based surprise and astonishment. In the central part of the paper we suggest how a formal model of surprise can be integrated with a formal model of belief change. We investigate the role of surprise in triggering the process of belief reconsideration. There are a number of models of surprise developed in the psychology of emotion. We provide several comparisons of our approach with those models.

The cognitive structure of surprise: looking for basic principles

First I shortly recapitulate the claim that autonomy is a relational notion in relation with agenthood and delegation; second, I lead back autonomy to the theory of dependence. Then I deal with the main claim of this paper arguing how the dimensions of the agent's autonomy derives from its architecture and from the theory of action.

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Cristiano Castelfranchi

Papers

The Micro-Macro Constitution of Power

Trust in information sources as a source for trust: a fuzzy approach

A fuzzy approach to a belief-based trust computation

The cognitive structure of surprise: looking for basic principles

Founding agent's 'autonomy' on dependence theory