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Some Elements for a Prehistory of Artificial Intelligence in the Last
Some Elements for a Prehistory of Artificial Intelligence in the Last

Research on the Application of Distributed Artificial Intelligence in
Research on the Application of Distributed Artificial Intelligence in

... groups of agents. The second one is about how to make intelligent communication and interaction and use all kinds of language and communication protocols, communication contents and time. Next is about how to ensure consistency in the interactions of decision, action, and the adjustment of local dec ...
A HIGH-SPEED ARCHITECTURE FOR BUILDING HYBRID MINDS
A HIGH-SPEED ARCHITECTURE FOR BUILDING HYBRID MINDS

Just an Artifact - Department of Computer Science
Just an Artifact - Department of Computer Science

original - Kansas State University
original - Kansas State University

...  Generic skeleton agent: Figure 2.4, R&N  function SkeletonAgent (percept) returns action  static: memory, agent’s memory of the world ...
Application of Artificial Intelligence in Finance
Application of Artificial Intelligence in Finance

... from memory for decision making. This is the principle underlying case-based reasoning technologies [4]. Case-based decision support can also analyze cases to extract patterns and discover knowledge hidden in data. Case-based information system helps to exploit data so that smarter business decision ...
Natural Computation in Finance
Natural Computation in Finance

... –  Likely to be useful when we have data but weak theory (perhaps some idea of the likely relevant variables but less idea how they link together) –  MLPs (universal approximators … but readability?) –  Today we will discuss the powerful methodologies of Genetic Programming (and associated grammar-b ...
Computer Vision and Remote Sensing – Lessons Learned
Computer Vision and Remote Sensing – Lessons Learned

... time – foreign world, such as Harlyn Baker or Robert Haralick, gave a flavour of techniques, not known in the core photogrammetric community, though adressing the same topics, specifically automatic surface reconstruction. The stimulating discussions with people from Pattern Recognition were the sta ...
Expert Systems and Knowledge Acquisition
Expert Systems and Knowledge Acquisition

... Selecting a suitable representation for the domain knowledge is one of the first problems encountered when building a KBS. There are some general principles that should guide this representation, though there is a considerable degree of disagreement among specialists in the field. The views presente ...
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Nishkam Ravi - Graduate Computing Resources

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penultimate version PDF - METU Department of Philosophy

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H - Computer Science | SIU

...  Combining the views of different experts. Large expert systems usually combine the knowledge and expertise of a number of experts. Unfortunately, experts often have contradictory opinions and produce conflicting rules. To resolve the conflict, the knowledge engineer has to attach a weight to each ...
PowerPoint 簡報 - 智慧型系統暨媒體處理實驗室
PowerPoint 簡報 - 智慧型系統暨媒體處理實驗室

... world and its knowledge, then explore the possibility and limitation of knowledge.  傳統的邏輯或數學體系是二元體系,無法處 理具有不確定性的問題或者對需要multiple truth values的問題之處理效率不足  你如何定義一個集合:老年人? ...
DEPARTMENT OF CYBERNETICS AND ARTIFICIAL INTELLIGENCE
DEPARTMENT OF CYBERNETICS AND ARTIFICIAL INTELLIGENCE

... and tools in decision support systems with emphasis on pattern recognition. It includes integrated chain of tasks starting with data acquisition, pre-processing and storing of input data, throughout knowledge discovery, to its presentation into decision making link in a suitable user interface. The ...
Claims and Challenges in Evaluating Human
Claims and Challenges in Evaluating Human

... (HLI). In the last five years, there has been a renewed interest in this pursuit with a significant increase in research in cognitive architectures and general intelligence as indicated by the first conference on Artificial General Intelligence. Although there is significant enthusiasm and activity, ...
Soft computing is an association of computing
Soft computing is an association of computing

... • Is a subfield of artificial intelligence (computational intelligence) that involves continuous optimization and combinatorial optimization problems. • Evolutionary computing uses iterative progress, such as growth or development in a population. This population is then selected in a guided random ...
PPT
PPT

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Informed Search.pps
Informed Search.pps

... • Expands nodes in the increasing order of costs • A* is optimally efficient – For a given heuristic, A* finds optimal solution with the fewest number of nodes expansion – Any algorithm that doesn’t expand nodes with f(n)
CS 415 – A.I.
CS 415 – A.I.

... BFS is equivalent to A* with heuristic h1 such that h1(x)=0 for all states x  This is always less than h*(x)  Lets call the number of tiles out of place, h2  This is also less than h*  But, we have h1<=h2<=h*  Thus, h2 is “more informed” than h1  Additionally, we can argue that calculation of ...
artificial intelligence research in particle accelerator control
artificial intelligence research in particle accelerator control

... eventually compared the results among several fundamentally different types of algorithms, including least squares and hybrid neural networks with real data that were obtained from Brookhaven National Laboratory. ...
November 2008_Introduction - School of Computer Science and
November 2008_Introduction - School of Computer Science and

... Problems in finance and business are amongst the hardest problems to be solved on computer systems: • Why are there now over 8,000 hedge funds? The reasons of course include economic and political developments, but it is also important that setting up a hedge fund is much easier in 2006 than it was ...
now
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Artificial Intelligence
Artificial Intelligence

... IF cart is on the left AND cart is going left THEN largely push cart to the right IF cart is on the left AND cart is not moving THEN slightly push cart to the right IF cart is on the left AND cart is going right THEN don’t push cart IF cart is centered AND cart is going left THEN slightly push cart ...
ppt - CSE, IIT Bombay
ppt - CSE, IIT Bombay

... Dennett, D. C. (1985) Can machines think? In: How we know, ed. ...
also available as Word 2000 ()
also available as Word 2000 ()

... While input data needs to be severely limited by focus and selection, it is also extremely important to obtain multiple views of reality – data from different feature extractors or senses. Provided that these different input patterns are properly associated, they can help to provide context for each ...
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AI winter

In the history of artificial intelligence, an AI winter is a period of reduced funding and interest in artificial intelligence research. The term was coined by analogy to the idea of a nuclear winter. The field has experienced several hype cycles, followed by disappointment and criticism, followed by funding cuts, followed by renewed interest years or decades later. There were two major winters in 1974–80 and 1987–93 and several smaller episodes, including: 1966: the failure of machine translation, 1970: the abandonment of connectionism, 1971–75: DARPA's frustration with the Speech Understanding Research program at Carnegie Mellon University, 1973: the large decrease in AI research in the United Kingdom in response to the Lighthill report, 1973–74: DARPA's cutbacks to academic AI research in general, 1987: the collapse of the Lisp machine market, 1988: the cancellation of new spending on AI by the Strategic Computing Initiative, 1993: expert systems slowly reaching the bottom, and 1990s: the quiet disappearance of the fifth-generation computer project's original goals.The term first appeared in 1984 as the topic of a public debate at the annual meeting of AAAI (then called the ""American Association of Artificial Intelligence""). It is a chain reaction that begins with pessimism in the AI community, followed by pessimism in the press, followed by a severe cutback in funding, followed by the end of serious research. At the meeting, Roger Schank and Marvin Minsky—two leading AI researchers who had survived the ""winter"" of the 1970s—warned the business community that enthusiasm for AI had spiraled out of control in the '80s and that disappointment would certainly follow. Three years later, the billion-dollar AI industry began to collapse.Hypes are common in many emerging technologies, such as the railway mania or the dot-com bubble. An AI winter is primarily a collapse in the perception of AI by government bureaucrats and venture capitalists. Despite the rise and fall of AI's reputation, it has continued to develop new and successful technologies. AI researcher Rodney Brooks would complain in 2002 that ""there's this stupid myth out there that AI has failed, but AI is around you every second of the day."" In 2005, Ray Kurzweil agreed: ""Many observers still think that the AI winter was the end of the story and that nothing since has come of the AI field. Yet today many thousands of AI applications are deeply embedded in the infrastructure of every industry."" He added: ""the AI winter is long since over.""
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