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

The study of multi-agent systems (MAS) focuses on systems in which many intelligent agents interact with each other. These agents are considered to be autonomous entities such as software programs or robots. Their interactions can either be cooperative (for example as in an ant colony) or selfish (as in a free market economy). This book assumes only basic knowledge of algorithms and discrete maths, both of which are taught as standard in the first or second year of computer science degree programmes. A basic knowledge of artificial intelligence would useful to help understand some of the issues, but is not essential. The books main aims are: To introduce the student to the concept of agents and multi-agent systems, and the main applications for which they are appropriate To introduce the main issues surrounding the design of intelligent agents To introduce the main issues surrounding the design of a multi-agent society To introduce a number of typical applications for agent technology

An Introduction to MultiAgent Systems.

What is modal logic

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An Introduction To The Event Related Potential Technique

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Art of electronics

Tabular Systems constitute a particular form of rule-based systems. They follow the pattern of Relational Databases and Attributive Decision Tables. In Extended Tabular Systems non-atomic values of attributes are allowed. In order to assure safe, reliable and efficient performance of such systems, analysis and verification of selected qualitative properties such as completeness, consistency and determinism should be carried out. However, verification of them after the design of a system is both costly and late. In this paper another solution is proposed. A graphical CASE-like tool supporting the design of Extended Tabular Systems and providing verification possibilities is presented. The tool uses a new rule specification paradigm: the so-called tab-trees, covering the advantages of Attributive Decision Tables and Decision Trees. In the system the verification stage is moved into the design process. The background theory and the idea of tab-trees are outlined and presentation of a practical tool, the Osiris System, is carried out.

Tab-Trees: A CASE Tool for the Design of Extended Tabular Systems

Business Processes describe the ways in which operations are carried out in order to accomplish the intended objectives of organizations. A process can be depicted using a modeling notation, such as Business Process Model and Notation. Process model can also describe operational aspects of every task; but in a properly designed model, the detailed aspects of low-level logic should be delegated to external services, especially to a Business Rule Engine. Business Rules support the specification of knowledge in a declarative manner and can be successfully used for specification of details for process tasks and events. However, there is no unified model of process integrated with rules that ensures data types consistency. Thus, the paper presents a formal description of the integration of Business Processes with Business Rules. We provide a general model for such integration as well as the model applied to a specific rule representation from the Semantic Knowledge Engineering approach.

/pdf/formal-model-of-business-processes-integrated-with-business-fw6ife3lek.pdf

Formal Model of Business Processes Integrated with Business Rules

This paper presents advances in Set Attributive Logic and its application to develop tabular rule-based systems within the XTT framework. The primary goal is to extend the expressive power of simple attributive languages so that it becomes satisfactory for complex applications, including the business rules support. A formal framework of extended Attributive Logic with Set Values over Finite Domains (ALSV(FD)) is presented and specific inference rules are provided with their corresponding prototype in PROLOG.

/pdf/xtt-rule-design-using-the-alsv-fd-1mbs3vcmlu.pdf

XTT+ rule design using the ALSV(FD)

The emotional state of the user is a new dimension in human-computer interaction, that can be used to improve the user experience. This is the domain of affective computing. In our work we focus on the applications of affective techniques in the design of video games. We assume that a change in the affective condition of a player can be detected based on the monitoring of physiological signals following the James-Lange theory of emotions. We propose the use of game design patterns introduced by Björk and Holopainen to build games. We identify a set of patterns that can be considered affective. Then we demonstrate how these patterns can be used in a design of a scroll-runner game. We address the problem of the calibration of measurements in order to reflect responses of individual users. We also provide results of practical experiments to verify our approach.

/pdf/affective-design-patterns-in-computer-games-scrollrunner-47b668c6ax.pdf

Affective Design Patterns in Computer Games. Scrollrunner Case Study

The paper discusses a proposal of a formal description of ARD+ a conceptual design and hierarchical refinement method for knowledge-based systems. Formalization of method syntax is given, as well as description of its semantics. ARD+ is a rework of the classic ARD presented elsewhere, that provides a conceptual design for rule-based systems. ARD+ is based on the concept of gradual design where consecutive design steps correspond to different knowledge

/pdf/towards-formalization-of-ard-conceptual-design-and-5dk36baxu0.pdf

Towards Formalization of ARD+ Conceptual Design and Refinement Method

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