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Machine learning for information retrieval: Neural networks
Machine learning for information retrieval: Neural networks

... finds information that the user has not explicitly requested but that is likely to be useful. Fox’s CODER system (Fox, 1987) consists of a thesaurus that was generated from the Handbook of Artificial Intelligence and Collin’s Dictionary. In CANSEARCH (Pollitt, 1987) a thesaurus is presented as a men ...
Testing of Various Embedded System with Artificial Intelligence
Testing of Various Embedded System with Artificial Intelligence

... All Rights Reserved © 2015 IJARCET ...
Cardoso, A., Veale, T., Wiggins, G.
Cardoso, A., Veale, T., Wiggins, G.

... fundamental inability to pin it down in formal terms. Ask most people the question “what is creativity?” and you are more likely to elicit an anecdote, an aphorism, or a metaphor, than you are a literal definition, least of all a definition that can contribute to the construction of a convincing com ...
full text pdf
full text pdf

... metrics for evaluation of progress are necessary. Metrics for assessing the achievement of humanlevel AGI are argued to be fairly straightforward, including e.g. the classic Turing test, and the test of operating a robot that can graduate from elementary school or university. On the other hand, metr ...
CSC 550: Introduction to Planning Fall 2004
CSC 550: Introduction to Planning Fall 2004

... Definition of Planning • Planning is reasoning about future events in order to establish a series of actions to accomplish a goal. - A common approach to planning is representing a current state and determining the series of actions necessary to reach the goal state. (or vice versa) – Problem solvi ...
Robotics
Robotics

... Machines beat humans at perfect information games but not yet at imperfect information ...
Organizing Data and Information
Organizing Data and Information

... Slide 10 ...
Constraint-Based Knowledge Representation for Individualized
Constraint-Based Knowledge Representation for Individualized

... states at a finer grain of detail does not enhance the pedagogical power of the system. In summary, the classical AI knowledge representations are unsuitable for student modeling because they require specificity and are designed for executability. These two related features are the main causes of th ...
(PPT, 202KB)
(PPT, 202KB)

... theories from psychology which look to understand how humans solve problems and represent knowledge ...
Artificial Cognitive Systems
Artificial Cognitive Systems

... •  ‘If an agent has knowledge that one of its actions will lead to one of its goals, then the agent will select that action’ ...
Report of research activities in fuzzy AI and medicine at
Report of research activities in fuzzy AI and medicine at

... may be at risk of sudden infant death syndrome (SIDS), yet are living a normal life within their family. The dif®culty of the problem relates to the restrictions on sensors (they should be completely non-obtrusive and should collect data without contact) and to the need to make a highly reliable ala ...
BrooksianVisions
BrooksianVisions

... • Dichotomy in robot implementation styles – Behaviour-based robotics (eg. Walter) – GOFAI (eg. Nilsson) ...
Brachet - UB Computer Science and Engineering
Brachet - UB Computer Science and Engineering

... – if we know how to explicitly teach some cognitive task to humans • e.g., play chess, do calculus, prove theorems ...
Tweety: A Comprehensive Collection of Java
Tweety: A Comprehensive Collection of Java

... (Thimm 2011; 2013b). We will discuss this package in more detail in Section 4. Relational Probabilistic Conditional Logic By combining both the Relational Conditional Logic and Probabilistic Conditional Logic libraries the Relational Probabilistic ...
in the control room of the banquet
in the control room of the banquet

Ten Years of the AAAI Mobile Robot Competition and Exhibition
Ten Years of the AAAI Mobile Robot Competition and Exhibition

... “dramatic robots,” prototypes for Mars rovers, and humanoid robots as well as the more typical looking research robots. The exhibition has served as a forum for researchers to give demonstrations of their latest work, sharing research more quickly with the community. The spirit of open discussion of ...
Why Machine Learning? - Lehrstuhl für Informatik 2
Why Machine Learning? - Lehrstuhl für Informatik 2

... Artificial Intelligence:Learning: Learning symbolic representation of concepts, ML as search problem , Prior knowledge + training examples guide the learning-process Bayesian Methods:Calculating probabilities of the hypotheses, Bayesian-classifier Theory of the computational complexity: Theoretical ...
comparative study of case based reasoning software
comparative study of case based reasoning software

... case-based reasoning approach, the proposed solution is revised according constraints of the problem. Then the proposed solution is repaired according to constraints. It is also modified for fulfilling the constraints of the problem. This phase of case-based reasoning boosts the excellence of the so ...
A Future for Agent Programming
A Future for Agent Programming

... should develop to maximise their adoption. This paper follows in a tradition of talks and panel sessions at agent programming workshops, including the Dagstuhl Seminar on Engineering Multi-Agent Systems [8] and the EMAS 2013 & 2014 workshops. In this section I briefly review some of this work. In [3 ...
Distributed multi-agent probabilistic reasoning with Bayesian networks
Distributed multi-agent probabilistic reasoning with Bayesian networks

... and consumes some computational resource. Each agent communicates with other agents to achieve the system’s goal cooperatively. Distributed artificial intelligence (DAI), a subfield of artificial intelligence, addresses the problems of designing and analyzing such ’large-grained’ coordinating multi- ...
Bibliography
Bibliography

... J. Bateman. The Theoretical Status of Ontologies in Natural Language Processing. In the Proceedings of the Workshop on Text Representation and Domain Modelling Ideas from Linguistics and AI. Technical University Berlin, October, 1991. ...
Methods of Artificial Intelligence – Fuzzy Logic
Methods of Artificial Intelligence – Fuzzy Logic

... voltage for example). They can be defined according to statistic data, however they are not arbitrarily associated, they are based on the criteria specific for the application. Expert system can calculate the value of output variables, as well as some other numeric values of input variables. It also ...
How to Get from Interpolated Keyframes to Neural
How to Get from Interpolated Keyframes to Neural

... Then keyframe Fj defines the target angles after transition Tji at time tn+1 = tn + ∆Tji . During the transition all target angles are linearly interpolated. This is no constraint, since the real hardware would smooth out more complex interpolating functions. Furthermore, non-linear distortions can ...
Conventional Ratio and Artificial Intelligence (AI) Diagnostic methods for DGA in Electrical Transformers
Conventional Ratio and Artificial Intelligence (AI) Diagnostic methods for DGA in Electrical Transformers

... is chosen as an example. The PPM of C2H2 in all cases equal zero. Table 8- Diagnostic data history for (220/66 Kv) transformer Some of the dissolved gases(ppm) ...
Lecture 05 Part A - First Order Logic (FOL)
Lecture 05 Part A - First Order Logic (FOL)

...  Last unification fails: only because x can’t take values John and Bill at the same time  Problem is due to use of same variable x in both sentences  Simple solution: Standardizing apart eliminates overlap of variables, e.g., Knows(z, Bill) ...
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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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