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

A survey on deep learning in medical image analysis

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.

/pdf/intelligent-agents-theory-and-practice-5dchj7p8pc.pdf

Intelligent Agents: Theory and Practice

From the Publisher:
An agent is an entity with domain knowledge, goals and actions. Multi-agent systems are a set of agents which
interact in a common environment. Multi-agent systems deal with the construction of complex systems involving multiple agents and their coordination. A multi-agent system (MAS) is a distributed computing system with
autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.

Introduction to Multiagent Systems

https://ic.unicamp.br/~rocha/pub/papers/automatic-fruit-and-vegetable-classification-from-images-compag-2010.pdf

Automatic fruit and vegetable classification from images

This paper discusses a conceptual model of groupware consisting of three complementary components or models: a description of the objects and operations on these objects available in the system; a description of the activities (and their orderings) that the users of the system can perform; and a description of the interface of users with the system, and with other users.

A conceptual model of groupware

Groupware and computer supported cooperative work

This paper presents a pair of role-based access control models for workflow systems, collectively known as the W-RBAC models. The first of these models, W0-RBAC is based on a framework that couples a powerful RBAC-based permission service and a workflow component with clear separation of concerns for ease of administration of authorizations. The permission service is the focus of the work, providing an expressive logic-based language for the selection of users authorized to perform workflow tasks, with preference ranking. W1-RBAC extends the basic model by incorporating exception handling capabilities through controlled and systematic overriding of constraints.

W-RBAC — A Workflow Security Model Incorporating Controlled Overriding of Constraints

The focus of traditional workflow management systems is on control flow within one process definition. The process definition describes how a single case (i.e. workflow instance) in isolation is handled. For many applications this paradigm is inadequate. Interaction between cases to support communication and collaboration is at least as important. This paper introduces and advocates the use of interacting proclets, i.e. lightweight workflow processes. By promoting interactions to first-class citizens it is possible to model complex workflows in a more natural manner. In addition, the expressive power and flexibility are improved compared to the more traditional workflow modeling languages.

Jacques Wainer

Papers

Automatic fruit and vegetable classification from images

A conceptual model of groupware

Groupware and computer supported cooperative work

W-RBAC — A Workflow Security Model Incorporating Controlled Overriding of Constraints

Proclets: a framework for lightweight interacting workflow processes