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CIS370 - Heppenstall.ca
CIS370 - Heppenstall.ca

... • There is no real generally-accepted definition of AI, but there are a few that come close. • Behaviour-centered is a “black-box,” because you don’t care how it works. Thoughtcentered allows you to look through the box to find out what it is “thinking.” • Turing said that, “by the year 2000, a syst ...
Die Grenzen des Verstehens – Voraussetzungen der
Die Grenzen des Verstehens – Voraussetzungen der

... What is Artificial General Intelligence up to? Perception, and what depends on it, is inexplicable in a mechanical way, that is, using figures and motions. Suppose there would be a machine, so arranged as to bring forth thoughts, experiences and perceptions; it would then certainly be possible to im ...
Advancing Multi-Context Systems by Inconsistency Management
Advancing Multi-Context Systems by Inconsistency Management

... pneumonia, b) a certain blood marker is present, and c) she has no known allergies. The ontology imports information on X-Ray and blood tests using bridge rules (Conto : xray(Sue)) ← (Cpatients : labresult (Sue, xray)). (Conto : marker (Sue)) ← (Cpatients : labresult (Sue, marker )). As the ontology ...
A New Fixpoint Semantics for General Logic Programs Compared
A New Fixpoint Semantics for General Logic Programs Compared

... models of comp(P2 ), but only one well-supported model {q} (called a grounded model in [12]) which is also the unique stable model and the iterated least model in the stratified semantics of [1] [38]. In [1] it is proved that the models of comp(P ) are exactly the supported models of P , so our char ...
Artificial Emotion Simulation Techniques for Intelligent Virtual
Artificial Emotion Simulation Techniques for Intelligent Virtual

... Adina Magda Florea, Ph.D. ...
project summary - Internet Mapping Services for San Diego Wildfire
project summary - Internet Mapping Services for San Diego Wildfire

... 1.2 Artificial Intelligence Research in Cartography Software agents developed from the research of artificial intelligence and distributed computing (Bigus and Bigus, 1998). This section will introduce the development of Artificial Intelligence (A. I.) particularly in the domain of Cartography in th ...
i S dS i S dS Fuzzy Logic, Sets and Systems Lecture 1 Introduction
i S dS i S dS Fuzzy Logic, Sets and Systems Lecture 1 Introduction

... and short computation time.  Thus, we need other technique, as supplementary to conventional ti l quantitative tit ti methods, th d for f manipulation i l ti off vague and d uncertain information, and to create systems that are much closer in spirit to human thinking. thinking Fuzzy logic is a stro ...
Perception Processing for General Intelligence
Perception Processing for General Intelligence

... 2. Subsymbolic representation and learning are the core of human intelligence; symbolic aspects of intelligence (a) emerge from the subsymbolic aspects as needed; or, (b) arise via a relatively simple, thin layer on top of subsymbolic intelligence, that merely applies subsymbolic intelligence in a s ...
Knowledge Management for Computational Intelligence Systems
Knowledge Management for Computational Intelligence Systems

... applyingthe theCBKM CBKMframework framework 5. K.-D. Althoff, A. Birk, G. von Wangenheim and C. Tautz, “Case-based reasoning for experimental software engineering,” in Case-Based Reasoning Technology - From Foundations to Applications, M. Lenz, B. Bartsch-Spörl, ...
Semantic Web Example
Semantic Web Example

... A property is symmetric. ...
Stop Using Introspection to Gather Data for the Design of... Modeling and Spatial Assistance
Stop Using Introspection to Gather Data for the Design of... Modeling and Spatial Assistance

... performance) the evidence is equivocal. However, in Knauff & Johnson-Laird (2002), it was argued that researchers often do not distinguish between ease of visualization and ease of constructing spatial representations. Rating studies, however, show that these factors can be separated. Their results ...
Automated Modelling and Solving in Constraint Programming
Automated Modelling and Solving in Constraint Programming

... relative merits of each formulation until its runtime behaviour is empirically analysed. The TAILOR system attempts to automate this by regarding model reformulation as a compilation-like process (Gent, Miguel, and Rendl 2007). An extremely innovative piece of work on automated reformulation is by C ...
Complex Preferences for Answer Set Optimization
Complex Preferences for Answer Set Optimization

Robot Learning, Future of Robotics
Robot Learning, Future of Robotics

... • The goal is to minimize the error between the network output and the desired output – This is achieved by adjusting the weights on the network ...
KBS88.pdf
KBS88.pdf

... recognition procedures must be supplied in the implementation language of the system. This allows an interface between the logical representation language and the computational data types supplied by the implementation language of the system. An example of a sort that can be declared via this mecha ...
Ethical Intelligence - The Unicist Research Institute
Ethical Intelligence - The Unicist Research Institute

cached
cached

... case library, where each case is a triplet, and using it to solve new problems (i.e., queries) through processes involving case retrieval, reuse, and revision. This can lead to the generation of new cases, which can be incorporated through a retention process. Conversati ...
Robotic-Spring06-3
Robotic-Spring06-3

... internal models to search for solutions and then try them out (M. Minsky) => deliberative model!  Planning became the tradition  Explicit symbolic representations  Hierarchical system organization  Sequential execution Robotics ...
Bodley_wsu_0251E_11404 - Washington State University
Bodley_wsu_0251E_11404 - Washington State University

... our inner self and our homes. I propose here to explore those potential effects by examining the embodiment and disembodiment of the android through contemporary popular culture examples. After an introduction to associated theories of humanism, posthumanism, and transhumanism, followed by a brief h ...
AAAI-2000 Workshop
AAAI-2000 Workshop

... a selected focus—providing an informal setting for active exchange among researchers, developers and users on topics of current interest. Members of all segments of the AI community are encouraged to submit proposals. To foster interaction and exchange of ideas, the workshops will be kept small, wit ...
Intelligent Environments
Intelligent Environments

... Intelligent Environments Decision-Making Techniques ...
and QUALITATIVE CONSTRAINTS - Dipartimento di Informatica
and QUALITATIVE CONSTRAINTS - Dipartimento di Informatica

... Different instantiations, depending on the types of constraints (and on the definitions of intersection and composition) ...
Introduction - Computer Science & Engineering
Introduction - Computer Science & Engineering

... • Project topics: an implementation of either: – a single robot system (involving complex behavior and demonstrated on a physical robot) or – a multi-robot system (involving cooperation/ communication/ coordination between robots and demonstrated in simulation) ...
APPLICATION OF ARTIFICIAL NEURAL NETWORK IN MARKET
APPLICATION OF ARTIFICIAL NEURAL NETWORK IN MARKET

Natural intelligence in design*
Natural intelligence in design*

... more concrete types of investigation, and from the more close to the more distant study of actual design practice. The studies have ranged through inexperienced or student designers, to experienced and expert designers, and even on to forms of non-human, artificial intelligence. All of these methods ...
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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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