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position tracking system to find shortest path to object using
position tracking system to find shortest path to object using

Document
Document

...  Artificial Intelligence (AI) has emerged as one of the most significance technologies of this century  subfield of computing science that is concerned with symbolic reasoning and problem solving, by manipulation of knowledge rather than mere data  classical ‘non-intelligent’ computer programs:  ...
Introduction to Information Technology Mind Tools for Your Future
Introduction to Information Technology Mind Tools for Your Future

... Neural networks, genetic algorithms, and cyborgs are three examples of strong AI. ...
Why Artificial Intelligence is the Future of Growth
Why Artificial Intelligence is the Future of Growth

... OF AI-LED GROWTH With AI as the new factor of production, it can drive growth in at least three important ways. First, it can create a new virtual workforce—what we call “intelligent automation.” Second, AI can complement and enhance the skills and ability of existing workforces and physical capital ...
What is AI?
What is AI?

... Logic, methods of reasoning, mind as physical system foundations of learning, language, rationality Formal representation and proof algorithms, computation, (un)decidability, (in)tractability, probability Utility, decision theory Physical substrate for mental activity Phenomena of perception and mot ...
PDF only
PDF only

... definitively establish or reject it is not available, or if evidence arrives that makes some other hypothesis more attractive. Uncertainty management schemes that insist on relatively rigid strategies like establish­ refine are not well suited to handle this situation. ...
PDF
PDF

... techniques. There has been some published work comparing some of these techniques. These comparisons help us reason about what techniques are most suitable for dynamic scheduling. Advantages and disadvantages of these techniques are provided by previous published work. Dispatching rules are easy and ...
CSC 599: Computational Scientific Discovery
CSC 599: Computational Scientific Discovery

... Not many Ci's (≈ 0)? On average few bits Each occurrence costs more than 1 bit but not many occurrences Lots of Ci's (≈ size(S))? Not many bits ...
Slide 1
Slide 1

... • A main motivation behind neural networks is the fact that symbolic rules do not reflect reasoning processes performed by humans. • Biological neural systems can capture highly parallel computations based on representations that are distributed over many neurons. • They learn and generalize from tr ...
Artificial Intelligence 4. Knowledge Representation
Artificial Intelligence 4. Knowledge Representation

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Reinforcement Learning and the Reward Engineering Principle
Reinforcement Learning and the Reward Engineering Principle

... merely stating what follows from these definitions, we also wish to examine the medium- and long-term implications of the adoption of these definitions by the artificial intelligence community. What difficulties will be faced by future researchers in this area? Reinforcement learning, as a conceptua ...
The RacerPro Knowledge Representation and Reasoning System
The RacerPro Knowledge Representation and Reasoning System

... Tbox which is “processed” only once). Tboxes (ontologies) and Aboxes are maintained using the RacerPro server system, which communicates with remote application programs using well-defined axiom manipulation languages or entailment query languages. In addition, a rule language (based on SWRL syntax) ...
The RacerPro Knowledge Representation and Reasoning System1
The RacerPro Knowledge Representation and Reasoning System1

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CS-INFO 372: Explorations in Artificial Intelligence
CS-INFO 372: Explorations in Artificial Intelligence

... I Building exact models of human cognition view from psychology and cognitive science II Developing methods to match or exceed human ...
Semantics and cognitive research, by Francois Rastier.
Semantics and cognitive research, by Francois Rastier.

... 3 A case in point is René Thom's semiophysics as developed by J. Petitot (1989, p. 218): "considered as the mathematical science of natural languages, theoretical linguistics is a natural science, more a physics than a logic". The expression natural languages that Petitot uses does not obliterate th ...
document
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... a knowledge representation • Much of the information and logic conveyed by language is dependent on context • Information exchange is not well defined • Not compositional (combining sentences may mean something different) • It is ambiguous ...
Quick recap of logic: Predicate Calculus - clic
Quick recap of logic: Predicate Calculus - clic

... • Artificial Intelligence research moved beyond first order logic in several directions: – Beyond using logic as a formalization of valid inference only, developing logics for non-valid (or NONMONOTONIC / UNCERTAIN) reasoning – Developing simpler logics in which inference can be done more efficientl ...
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PDF

... requirements and modify it. Although this process looks feasible, it has not been demonstrated in software engineering to any great extent. Closely related to analogical reasoning techniques is Case-based reasoning (CBR). CBR is based upon the premise that similar problems are best solved with simil ...
Lecture Notes in Computer Science
Lecture Notes in Computer Science

... through their individual expression and collective competition and cooperation. Agents in such societies employ heterogeneous algorithms, specialised for the goal they are addressing or the task they are designed to carry out. Within the society however, all agents generally present a standard inter ...
Simulating Virtual Humans Across Diverse Situations
Simulating Virtual Humans Across Diverse Situations

... human users, and persistent refers to the fact that every virtual human within a simulation is modelled (at least to some extent) at all times. The architecture has three key components - the Schedule Unit, the Role-Passing Unit and the µ-SIC System (or Social Unit). These three components are conne ...
Candidate for Chair Yolanda Gil University of Southern California
Candidate for Chair Yolanda Gil University of Southern California

... believe that AI can deliver. This is my second time around, and I hope my familiarity with the SIGART organization will provide me with better opportunities to work with the rest of the SIGART team in highlighting such opportunities, providing forums (workshops and maybe even conferences) for discus ...
AI Surveying: Artificial Intelligence In Business
AI Surveying: Artificial Intelligence In Business

... organizing my discussion based on AI methods very problematic, and so a decision has been made to organise this chapter into the following business areas: Customer Relationship Management (CRM), Company Management, Production Management, and Finance Management. Under each heading, where needed, ther ...
here - FER
here - FER

... entirely known environment, making it a very real possibility that a hardwired behaviour may at one point become inappropriate or even outright negatively affect performance. The benefits of multiagent reinforcement learning arise primarily from the distributed nature of the multiagent system, and i ...
The computational modeling of analogy-making
The computational modeling of analogy-making

An Introduction to Deep Learning
An Introduction to Deep Learning

... inspired architectures, imitating the processing of “simple” and “complex” cortical cells which respectively extract orientations information (similar to a Gabor filtering) and compositions of these orientations. The main idea of convolutional networks is to combine local computations (convolution of ...
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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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