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AgentWeb: Resources: Introductory Material
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- Mobile Agents and the Future of the Internet
664 - Mobile Agents and the Future of the Internet , David Kotz and Bob Gray, ACM Operating Systems Review, 33(3), Aug 1999, pp 7-13.,-- an update of aposition paper that appeared at the Workshop Mobile Agents in the Context of Competition and Cooperation (MAC3) at Autonomous Agents, May1, 1999, in Seattle, Washington, USA.
- Is There an Intelligent Agent in Your Future?
648 - Jim Hendler, Is There an Intelligent Agent in Your Future?, Naure, March 11, 1999. A short article on agent technology for a general scientific audience.
- Game Theory: An Introductory Sketch
54 - Roger A. McCain (Drexel University, Philadelphia, Pennsylvania)maintains a web site titled "Game Theory: An Introductory Sketch" which provides a readable elementaryexposition of basic game theory principles for non-specialists.
- An Introduction To Artificial Life, Moshe Sipper
21 - Moshe Sipper, An Introduction To Artificial Life, inExplorations in Artificial Life (special issue of AI Expert),pages 4-8, September, 1995. Miller Freeman.
- MITECS: Theory of Mind
18 - "Understanding other people is one of the most fundamentalhuman problems. We know much less, however, about ourability to understand other minds than about our ability tounderstand the physical world. The branch of cognitive sciencethat concerns our understanding of the minds of ourselves andothers has come to be called "theory of mind," though it shouldperhaps be called " theory of theory of mind." It involvespsychological theorizing about our ordinary, intuitive, "folk"understanding of the mind. ..." -- Alison Gopnik
- MITECS: Knowledge Representation
19 - "Knowledge representation (KR) refers to the general topic ofhow information can be appropriately encoded and utilized incomputational models of cognition. ..." -- Patrick Hayes
- MITECS: Machine Learning
18 - "The goal of machine learning is to build computer systems thatcan adapt and learn from their experience. Different learningtechniques have been developed for different performancetasks. The primary tasks that have been investigated areSUPERVISED LEARNING for discrete decision-making,supervised learning for continuous prediction,REINFORCEMENT LEARNING for sequential decisionmaking, and UNSUPERVISED LEARNING. ..." -- Tom Dietterich
- MITECS: Reinforcement Learning
16 - "Reinforcement learning is an approach to artificial intelligencethat emphasizes learning by the individual from its interactionwith its environment. This contrasts with classical approachesto artificial intelligence and MACHINE LEARNING, which havedownplayed learning from interaction, focusing instead onlearning from a knowledgeable teacher, or on reasoning from acomplete model of the environment. ..." -- Richard S. Sutton
- MITECS: Unsupervised Learning
14 - "Unsupervised learning studies how systems can learn torepresent particular input patterns in a way that reflects thestatistical structure of the overall collection of input patterns. Bycontrast with SUPERVISED LEARNING orREINFORCEMENT LEARNING, there are no explicit targetoutputs or environmental evaluations associated with eachinput; rather, the unsupervised learner brings to bear priorbiases as to what aspects of the structure of the input shouldbe captured in the output. ..." -- Peter Dayan
- MITECS: Self-Organizing Systems
15 - "Self-organization refers to spontaneous ordering tendenciessometimes observed in certain classes of complex systems,both artificial and natural. ..." -- David Depew and Bruce Weber
- MITECS: Artificial Life
14 - "Artificial life (A-Life) uses informational concepts and computermodeling to study life in general, and terrestrial life in particular.It aims to explain particular vital phenomena, ranging from theorigin of biochemical metabolisms to the coevolution ofbehavioral strategies, and also the abstract properties of lifeas such ("life as it could be"). ..." -- Margaret A. Boden
- MITECS: Metareasoning
14 - "Metareasoning is reasoning about reasoning -- in its broadestsense, any computational process concerned with theoperation of some other computational process within thesame entity...." -- Stuart J. Russell
- MITECS: Propositional Attitudes
13 - "Propositional attitudes are mental states with representationalcontent. Belief is the most prominent example of apropositional attitude. Others include intention, wishing andwanting, hope and fear, seeming and appearing, and tacitpresupposition. ..." -- Robert Stalnaker
- MITECS: Intentional Stance
12 - "The intentional stance is the strategy of interpreting thebehavior of an entity (person, animal, artifact, or the like) bytreating it as if it were a rational agent that governed its"choice" of "action" by a "consideration" of its "beliefs" and"desires." ..." -- Daniel Dennett
- MITECS: Mobile Robots
14 - -- Reid G. Simmons
- MITECS: Multiagent Systems
37 - Multiagent systems are distributed computer systems in which the designersascribe to component modules autonomy, mental state, and othercharacteristics of agency. -- -- Michael P. Wellman
- MITECS: Intelligent Agent Architecture
33 - Intelligent agent architecture is a model of an intelligent information-processingsystem defining its major subsystems, their functional roles, and the flow ofinformation and control among them. -- Stanley J. Rosenschein
- MITECS: Rational Agency
14 - "In philosophy of mind, rationality is conceived of as a coherence requirement onpersonal identity: roughly, "No rationality, no agent." The agent must have ameans-ends competence to fit its actions or decisions, according to its beliefsor knowledge-representation, to its desires or goal-structure....", Christopher Cherniak, MITECS
- The current landscape of Agent Communication Languages
52 - Yannis Labrou, Tim Finin and Yun Peng, The current landscape of AgentCommunication Languages, Intelligent Systems, volume 14, number 2,March/April 1999, IEEE Computer Society.
- Communications of the ACM: March 1999
- Internet Computing: July/August 1997
- A gentle intoduction to agents
- About AI
18 - a special web site provided by the AmericanAssociation for Artificial Intelligence (AAAI) for students,teachers, journalists, and everyone who would like to learnmore about what artificial intelligence is, and what AIscientists do.
- Agents working together
17 - A short article by Don Barker written for PC AI on agent communication and cooperation.
- Machine Learning in Automated Text Categorisation
17 - Fabrizio Sebastiani, Machine Learning in Automated Text Categorisation, 1999, submitted for publication. *paper*
- Artificial Life (tutorial)
22 - Ray, T S, In press, Artificial Life, In: ``From Atoms to Mind'', Gilbert, Walter, and GlaucoTocchini Valentini, [eds]. Istituto della Enciclopedia Italiana Treccani. Rome.
- Introduction to genetic algorithms with Java applets
19 - An overview of genetic algorithms illustrated with java applets, by Marek Obitko, Czech Technical University.
- Where Do Intelligent Agents Come From?
23 - Intelligence From Dumb Agents, by Cristobal Baray and Kyle Wagner, ACM Crossroads, v5n4, summer 1999.
- AOIS Glossary
16 - "The AOIS Glossary" is a hypertext glossarylisting the most fundamental terms and concepts that are relevant to the general theme "Agent-Orientation in Information Systems".
- Towards a Standardization of Multi-Agent System Frameworks
28 - An article by Roberto A. Flores-Mendez in the ACM Crossroads magazine's special issue on agents.