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

Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

How to Do Things With Words

Words as social tools: Language, sociality and inner grounding in abstract concepts.

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.

/pdf/trust-in-information-sources-as-a-source-for-trust-a-fuzzy-e3wkpoei0b.pdf

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

http://gral.istc.cnr.it/borghi/pezzulo-barca-lambertibocconi-borghi-climbers%26affordances-Brain%26Cognition.pdf

When affordances climb into your mind: Advantages of motor simulation in a memory task performed by novice and expert rock climbers

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

Studies on how “the social mind” works reveal that cognitive agents engaged in joint actions actively estimate and influence another’s cognitive variables and form shared representations with them. (How) do shared representations enhance coordination? In this paper, we provide a probabilistic model of joint action that emphasizes how shared representations help solving interaction problems. We focus on two aspects of the model. First, we discuss how shared representations permit to coordinate at the level of cognitive variables (beliefs, intentions, and actions) and determine a coherent unfolding of action execution and predictive processes in the brains of two agents. Second, we discuss the importance of signaling actions as part of a strategy for sharing representations and the active guidance of another’s actions toward the achievement of a joint goal. Furthermore, we present data from a human-computer experiment (the Tower Game) in which two agents (human and computer) have to build together a tower made of colored blocks, but only the human knows the constellation of the tower to be built (e.g., red-blue-red-blue-
 $$\ldots$$
 ). We report evidence that humans use signaling strategies that take another’s uncertainty into consideration, and that in turn our model is able to use humans’ actions as cues to “align” its representations and to select complementary actions.

Giovanni Pezzulo

Papers

Words as social tools: Language, sociality and inner grounding in abstract concepts.

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

When affordances climb into your mind: Advantages of motor simulation in a memory task performed by novice and expert rock climbers

A fuzzy approach to a belief-based trust computation

What should I do next? Using shared representations to solve interaction problems