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Knowledge
Knowledge

... • Rockefeller-sponsored Institute at Dartmouth College, Summer 1956 – John McCarthy, Dartmouth (->MIT->Stanford) – Marvin Minsky, MIT (geometry) – Herbert Simon, CMU (logic) – Allen Newell, CMU (logic) – Arthur Samuel, IBM (checkers) – Alex Bernstein, IBM (chess) – Nathan Rochester, IBM (neural netw ...
Distributed case-based reasoning
Distributed case-based reasoning

... In contrast to single-agent CBR systems, multi-agent systems distribute the case base itself and/or some aspect(s) of the CBR cycle (Retrieve, Reuse, Revise, Retain) among several (more than one) agents. A multi-agent CBR system in this context is a distributed system of cooperative agents, where in ...
The Fourth International Workshop on Nonmonotonic Reasoning
The Fourth International Workshop on Nonmonotonic Reasoning

... and connections between classical nonmonotonic logics and probability-theoretic logics, conditional logics, and logic programming. Computational issues occupied the lion’s share of the workshop, both during the sessions and offline, in marked contrast to earlier workshops. In part, this situation ca ...
Developing Effective Robot Teammates for Human
Developing Effective Robot Teammates for Human

... the Collaborative Workbench platform, designed for shared workspace, close proximity human-robot teaming exercises. estimation, inferring other agents’ intentions (Hayes and Scassellati 2013b), collaborative manipulation, and legibly conveying internal knowledge and understanding. In particular, we ...
Intelligent Automation
Intelligent Automation

... indistinguishable from those with a human, then the machine can be said to be 'intelligent'. Despite subsequent debate about the value of the now famous “Turing Test”, we think this makes for a reasonable benchmark. And while it seems an eminently reachable goal today, the road to this point has bee ...
CALL FOR PAPERS, WORKSHOPS, AND TUTORIALS Thirteenth
CALL FOR PAPERS, WORKSHOPS, AND TUTORIALS Thirteenth

... will be co-located with the AAMAS 2012 conference. Accepted technical papers and invited talks will be presented from June 4 through June 6; tutorials and workshops will be held on June 7 and June 8. The natural focus of the conference is on computer science issues, but the conference is interdiscip ...
A Case Study in Developmental Robotics
A Case Study in Developmental Robotics

... Behaviourist Congress, and entitled “AI (re-) discovers behaviorism and other analogies” (Stojanov et al., 1996a) we related the situation in AI in the mid 80’s to the situation in psychology in the ‘30s, when the so called “physics envy” led to the rise of behaviorism. The school of behaviorism dis ...
ai-lect2
ai-lect2

... • Task-specific & specialized: well-defined goals and environment • The notion of an agent is meant to be a tool for analyzing systems, not an absolute characterization that divides the world into agents and non-agents. Much like, e.g., object-oriented vs. imperative program design approaches. ...
canonical3
canonical3

Chapter 2
Chapter 2

... acquires new cases to improve and evolve its decision-making abilities. Further, the representation of a case has various forms, such as an example or even a story, as long as it can be recognized by a reasoner in a specific domain. Semantically, a case represents both a specific piece of knowledge ...
NLP - DePaul University
NLP - DePaul University

... in Knight Rider ...
Jos Uiterwijk - Van der Waals
Jos Uiterwijk - Van der Waals

... 2. Domain is fixed, by which programs are easily comparable, both with other programs and with humans. By the nature of a game it is easy to test if a new technique is “better”. 3. Games are typical for human intelligence (Goethe: chess is the touchstone of the intellect). This explains the interest ...
Storyboard Concept - Stanford Artificial Intelligence Laboratory
Storyboard Concept - Stanford Artificial Intelligence Laboratory

... [Courtesy of David Shim] ...
Principles of Information Systems, Ninth Edition
Principles of Information Systems, Ninth Edition

... Principles and Learning Objectives • Knowledge management allows organizations to share knowledge and experience among their managers and employees • Artificial intelligence systems ปั ญญาประดิษฐ์ form a broad and diverse set of systems that can replicate human decision making for certain types of ...
Mapping Between Agent Architectures and Brain Organization
Mapping Between Agent Architectures and Brain Organization

... [34, 65].) This sort of temporal modularity is not yet well understood, but it could have implications for individual differences in intellectual task performance such as insight and metaphoric reasoning. ...
ID-CSH - Truman State University
ID-CSH - Truman State University

... no way to do these.  Any human output, including that with specified complexity, can be produced by mechanisms including chance. ...
Computational Generation of Dream-like Narrative
Computational Generation of Dream-like Narrative

Knowledge Processing for Cognitive Robots
Knowledge Processing for Cognitive Robots

... knowledge acquisition, representation, reasoning and learning, and make them work for autonomous robots by grounding the AI methods into the robot’s perception and action mechanisms and the data structures of the robot and by developing “satisficing” methods [22] that work under reasonable assumptio ...
d - Fizyka UMK
d - Fizyka UMK

... learning algorithms [predictions by economists] is the same. • Averaged over all target functions no learning algorithm yields generalization error that is superior to any other. • There is no problem-independent or “best” set of features. “Experience with a broad range of techniques is the best ins ...
Expert Systems 2
Expert Systems 2

... • ES have an explanation facility. • An expert system is different from conventional programs in the sense that program control and knowledge are separate. We can change one while affecting the other minimally. • ”There is a clear separation of general knowledge about the problem (the rules forming ...
cognitive theories of learning as the basis for didactic metapro
cognitive theories of learning as the basis for didactic metapro

... In these situations, teachers play a role of a “mediator” – agent helping students to choose proper strategies of learning, manners of solving problems and self-acquiring of knowledge. In the role, the teacher and students may be supported by didactic (hypermedia) computer programmes which shall inc ...
Neural Network and Fuzzy Logic
Neural Network and Fuzzy Logic

... system’ that is systems that exhibit the characteristics we associated with intelligence in human behavior. Artificial intelligence is the branch of computer science that is concerned with automation of intelligent behavior. AI have many of technologies, some of them are neural network, Fuzzy logic, ...
Master of Science DEGREE IN COMPUTER SCIENCE
Master of Science DEGREE IN COMPUTER SCIENCE

... in the lives of everyone. Computer Science offers a path to careers in nearly every aspect of human endeavor from science and technology to art, music and entertainment to medicine, to robotics to psychology and philosophy. If you want to do research in this exciting field, Lamar University’s Comput ...
Case-Based Reasoning and Expert Systems
Case-Based Reasoning and Expert Systems

... We need models for both integrated problem solving and for natural growing And even while growing such a CBR based expert system, it must be able to reasonably solve problems, maybe not all and not the difficult ones © 2012 DFKI GmbH ...
An Expert System Approach for determine the stage of UiTM Perlis
An Expert System Approach for determine the stage of UiTM Perlis

... Abstract: The Palapes cadets are one of the uniform organizations in UiTM Perlis for extra-curricular activities. The Palapes cadets arrange their organization in a hierarchy according to grade. Senior Uniform Officer (SUO) is the highest rank, followed by Junior Uniform Officer (JUO), sergeant, cor ...
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History of artificial intelligence

The history of artificial intelligence (AI) began in antiquity, with myths, stories and rumors of artificial beings endowed with intelligence or consciousness by master craftsmen; as Pamela McCorduck writes, AI began with ""an ancient wish to forge the gods.""The seeds of modern AI were planted by classical philosophers who attempted to describe the process of human thinking as the mechanical manipulation of symbols. This work culminated in the invention of the programmable digital computer in the 1940s, a machine based on the abstract essence of mathematical reasoning. This device and the ideas behind it inspired a handful of scientists to begin seriously discussing the possibility of building an electronic brain.The field of AI research was founded at a conference on the campus of Dartmouth College in the summer of 1956. Those who attended would become the leaders of AI research for decades. Many of them predicted that a machine as intelligent as a human being would exist in no more than a generation and they were given millions of dollars to make this vision come true. Eventually it became obvious that they had grossly underestimated the difficulty of the project. In 1973, in response to the criticism of James Lighthill and ongoing pressure from congress, the U.S. and British Governments stopped funding undirected research into artificial intelligence. Seven years later, a visionary initiative by the Japanese Government inspired governments and industry to provide AI with billions of dollars, but by the late 80s the investors became disillusioned and withdrew funding again. This cycle of boom and bust, of ""AI winters"" and summers, continues to haunt the field. Undaunted, there are those who make extraordinary predictions even now.Progress in AI has continued, despite the rise and fall of its reputation in the eyes of government bureaucrats and venture capitalists. Problems that had begun to seem impossible in 1970 have been solved and the solutions are now used in successful commercial products. However, no machine has been built with a human level of intelligence, contrary to the optimistic predictions of the first generation of AI researchers. ""We can only see a short distance ahead,"" admitted Alan Turing, in a famous 1950 paper that catalyzed the modern search for machines that think. ""But,"" he added, ""we can see much that must be done.""
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