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

Contempt and disgust: the emotions of disrespect

'Autonomy', with 'interaction' the central issue of the new Agent-based AI paradigm, has to be recollected to the internal and external powers and resources of the Agent. Internal resources are specified by the Agent architecture (and by skills, knowledge, cognitive capabilities, etc.); external resources are provided (or limited) by accessibility, competition, pro-social relations, and norms. 'Autonomy' is a relational and situated notion: the Agent - as for a given needed resource and for a goal to be achieved- is autonomous from the environment or from other Agents. Otherwise it is 'dependent' on them. We present this theory of Autonomy (independence, goal autonomy, norm autonomy, autonomy in delegation, discretion, control autonomy, etc.) and we examine how acting within a group or organization reduces and limits the Agent autonomy, but also how this may provide powers and resources and even increase the Autonomy of the Agent.

Founding autonomy: The dialectics between (social) environment and agent's architecture and powers

A crucial part of the intelligence that smart environments should display is a specific form of social intelligence: the ability to read human behavior and its traces in terms of underlying intentions and assumptions. Such ability is crucial to enable human users to tacitly coordinate and negotiate with smart and proactive digital environments. In this paper, the authors argue that the necessary tool for this ability is behavioral and stigmergic implicit (i.e. non-conventional) communication. The authors present a basic theory of such a fundamental interactive means—the theory of Behavioral Implicit Communication (BIC).

Behavioral Implicit Communication (BIC): Communicating with Smart Environments via our Practical Behavior and Its Traces

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

In this paper, the role of incentives in social order is questioned, based on a notion of incentive as additional individual utility, provided by an external entity, to actions achieving global utility. Two notions of norms are compared: (1) inputs which modify agents’ decisions through incentives (sanctions) and (2) prescriptions to execute obligatory action for intrinsic motivations. Two types of agents which reason upon norms are also compared: (1) incentive based rational deciders, and (2) normative agents which are prescribed to execute norms for intrinsic reasons. The two types of agents are expected to have a different impact on norm compliance. Under suboptimal conditions of application of sanctions (uncertian punishment), transgression is expected to propagate more easily and rapidly among incentive-based agents than among normative agents. In particular, incentive-based agents are expected to show a fast decline and even a collpase in compliance with the norms. Normative agents are expected to exhibit an oscillating behaviour, or at least a graceful degradation of compliance. Finally, the role of incentives is shown to have a lesser impact on natural social agents than expected by a model of rational decision. What is worse, incentives have been shown to produce even negative effects on several aspects of social learning and norm compliance.

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Cristiano Castelfranchi

Papers

Contempt and disgust: the emotions of disrespect

Founding autonomy: The dialectics between (social) environment and agent's architecture and powers

Behavioral Implicit Communication (BIC): Communicating with Smart Environments via our Practical Behavior and Its Traces

A fuzzy approach to a belief-based trust computation

Are incentives good enough to achieve (info) social order