Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

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Reinforcement Learning: An Introduction

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 contract net protocol has been developed to specify problem-solving communication and control for nodes in a distributed problem solver. Task distribution is affected by a negotiation process, a discussion carried on between nodes with tasks to be executed and nodes that may be able to execute those tasks.

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The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver

This survey characterizes an emerging research area, sometimes called coordination theory, that focuses on the interdisciplinary study of coordination. Research in this area uses and extends ideas about coordination from disciplines such as computer science, organization theory, operations research, economics, linguistics, and psychology.A key insight of the framework presented here is that coordination can be seen as the process of managing dependencies among activities. Further progress, therefore, should be possible by characterizing different kinds of dependencies and identifying the coordination processes that can be used to manage them. A variety of processes are analyzed from this perspective, and commonalities across disciplines are identified. Processes analyzed include those for managing shared resources, producer/consumer relationships, simultaneity constraints, and task/subtask dependencies.Section 3 summarizes ways of applying a coordination perspective in three different domains:(1) understanding the effects of information technology on human organizations and markets, (2) designing cooperative work tools, and (3) designing distributed and parallel computer systems. In the final section, elements of a research agenda in this new area are briefly outlined.

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The interdisciplinary study of coordination

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

Enterprise Information Space: User's View, Developer's View, and Market Approach


 Ongoing growth in the volume of raw data generated by digitized oil and gas operations has been widely documented (Spath, 2014). What may be less apparent is that the industry is also authoring dramatically more unstructured content—interpretations, learnings, case studies, etc.—on an annual basis. This is not surprising, as anecdotal evidence suggests that most decisions are taken on the basis of unstructured data (Quaadgras & Beath, 2011) (Garland, 2017) (Palkowsky, 2005) (Haines, Shaughnessy, & Briggs, 2006) (Hollingsworth & Schey, 2017). At the Society of Petroleum Engineers, the number of new papers published annually has followed an exponential growth curve that doubles approximately every 10-11 years, beginning as far back as the early 1950's.

AI Supports Information Discovery and Analysis in an SPE Research Portal

Machine Intelligence, 10, J.E. Hayes, D. Michie, Y.-H. Pao (Eds.). Ellis Horwood, Chichester, UK (1982)

/pdf/inconsistencies-in-a-user-interface-arise-primarily-when-o6zekxi94q.pdf

Inconsistencies in a user interface arise primarily when implicit knowledge about individual applications is encoded. Separation of user interface from applications, together with declarative descriptions of applications, offers a solution. Declarative Task Description as a User-Interface Structuring Mechanism

Intelligent assistants facilitate design and construction of complex software. In this article, we propose a model for an intelligent assistant to aid in building one kind of software-knowledge-based sys- tems-and discuss a preliminary implementation. The assistant partic- ipates in knowledge-based system (KBS) construction, including ac- quisition of an initial model of a problem domain, acquisition of control and task-specific inference knowledge, testing and validation, and long- term maintenance of encoded knowledge. We present a hypothetical scenario in which the assistant and a KBS designer cooperate to create an initial domain model, and discuss five categories of knowledge the assistant requires to offer such help. We then discuss two software technologies upon which the assistant is based-an object-oriented pro- gramming language, and a user-interface framework.

http://ieeexplore.ieee.org/iel1/32/458/00009063.pdf

Reid G. Smith

Papers

Enterprise Information Space: User's View, Developer's View, and Market Approach

AI Supports Information Discovery and Analysis in an SPE Research Portal

Machine Intelligence, 10, J.E. Hayes, D. Michie, Y.-H. Pao (Eds.). Ellis Horwood, Chichester, UK (1982)

Knowledge-Based Assistant