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

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Modern Applied Statistics With S

(1963). Cybernetics, or Control and Communication in the Animal and the Machine. Technometrics: Vol. 5, No. 1, pp. 128-130.

Cybernetics, or Control and Communication in the Animal and the Machine

The Study of Instinct

Trust is a fundamental concern in large-scale open distributed systems. It lies at the core of all interactions between the entities that have to operate in such uncertain and constantly changing environments. Given this complexity, these components, and the ensuing system, are increasingly being conceptualised, designed, and built using agent-based techniques and, to this end, this paper examines the specific role of trust in multi-agent systems. In particular, we survey the state of the art and provide an account of the main directions along which research efforts are being focused. In so doing, we critically evaluate the relative strengths and weaknesses of the main models that have been proposed and show how, fundamentally, they all seek to minimise the uncertainty in interactions. Finally, we outline the areas that require further research in order to develop a comprehensive treatment of trust in complex computational settings.

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Trust in multi-agent systems

In this paper we develop a notion of "objective trust" for Software Agents, that is trust of, or between, Agents based on actual experiences between those Agents Experiential objective trust allows Agents to make decisions about how to select other Agents when a choice has to be made We define a mechanism for such an "objective Trust-Based Agent" (oTB-Agent), and present experimental results in a simulated trading environment based on an Intelligent Networks (IN) scenario The trust one Agent places in another is dynamic, updated on the basis of each experience We use this to investigate three questions related to trust in Multi-Agent Systems (MAS), first how trust affects the formation of trading partnerships, second, whether trust developed over a period can equate to "loyalty" and third whether a less than scrupulous Agent can exploit the individual nature of trust to its advantage

Experiments in Building Experiential Trust in a Society of Objective-Trust Based Agents

This paper describes the Region Occlusion Calculus (ROC-20), that can be used to model spatial occlusion and the effects of motion parallax of arbitrary shaped objects. ROC-20 assumes the region based ontology of RCC-8 and extends Galton's Lines of Sight Calculus by allowing concave shaped objects into the modelled domain. This extension is used to describe the effects of mutually occluding bodies. The inclusion of van Benthem's axiomatisation of comparative nearness facilitates reasoning about relative distances between occluding bodies. Further, an envisionment table is developed to model sequences of occlusion events enabling reasoning about objects and their images formed in a changing visual field.

/pdf/from-images-to-bodies-modelling-and-exploiting-spatial-3k4bgpakrx.pdf

From images to bodies: modelling and exploiting spatial occlusion and motion parallax

This paper presents a programmable logic-based agent control system that interleaves planning, plan execution and perception. In this system, a program is a collection of logical formulae describing the agent's relationship to its environment. Two such programs for a mobile robot are described -- one for navigation and one for map building -- that share much of their code. The map building program incorporates a rudimentary approach to the formalisation of epistemic fluents, knowledge goals, and knowledge producing actions.

/pdf/high-level-robot-control-through-logic-35nixqdlra.pdf

High-Level Robot Control through Logic

In this paper we develop a notion of objective trust for Software Agents, that is trust of, or between, Agents based on actual experiences between those Agents. Experiential objective trust allows Agents to make decisions about how to select other Agents when a choice has to be made. We define a mechanism for such an objective Trust-Based Agent (oTB-Agent), and present experimental results in a simulated trading environment based on an Intelligent Networks (IN) scenario. The trust one Agent places in another is dynamic, updated on the basis of each experience. We use this to investigate three questions related to trust in Multi-Agent Systems (MAS), first how trust affects the formation of trading partnerships, second, whether trust developed over a period can equate to loyalty and third whether a less than scrupulous Agent can exploit the individual nature of trust to its advantage.

Experiments in building experiential trust in a society of objective-trust based Agents

Previous work on event calculus planning has viewed it as an abductive task, whose implementation through abductive logic programming reduces to partial order planning. By contrast, this paper presents a formal framework for tackling event calculus planning through satisfiability, in the style of Kautz and Selman. A provably correct conjunctive normal form encoding for event calculus planning problems is supplied, rendering them soluble by an off-the-shelf SAT solver.

/pdf/event-calculus-planning-through-satisfiability-1jvvoxslmm.pdf

Mark Witkowski

Papers

Experiments in Building Experiential Trust in a Society of Objective-Trust Based Agents

From images to bodies: modelling and exploiting spatial occlusion and motion parallax

High-Level Robot Control through Logic

Experiments in building experiential trust in a society of objective-trust based Agents

Event Calculus Planning Through Satisfiability