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Machine Super Intelligence
Machine Super Intelligence

... The main theoretical limitation of Solomonoff induction is that it only addresses the problem of passive inductive learning, in particular sequence prediction. Whether the agent’s predictions are correct or not has no effect on the future observed sequence. Thus the agent is passive in the sense tha ...
Module 2
Module 2

... automate the intellectual tasks on which they have been working all their lives. Similarly, workers in AI can choose to apply their methods to any area of human intellectual endeavour. In this sense, it is truly a universal field. ...
eref Saglroglu Intelligent Systems Research Group, Contra
eref Saglroglu Intelligent Systems Research Group, Contra

... the control difficult such as when a model of the system is not available, the system may change with time, or the controller itself may change with time due to component failures. Use of neural networks in robot control offers a new p~omising direction for solving some of the most difficult control ...
Sociology: Computational Organization Theory Keywords
Sociology: Computational Organization Theory Keywords

... way in which the agent’s knowledge, and the “shared” knowledge is represented. Other critical components include the way in which this knowledge is searched and/or activated. Some research in distributed artificial intelligence employs general models of cognition that make very strong claims about t ...
Constraints and AI Planning
Constraints and AI Planning

... subj to DefnAddOnlyIf {f in FLU, a in ADD[f] diff PRE[f],t in 1..T}: Add[f,t] >= Do[a,t]; subj to DefnAddIf {f in FLU, t in 1..T} Add[f,t] <= sum {a in ADD[f] diff PRE[f]} Do[a,t]; This is a direct transcription of mathematical statements that appear in a conventional formulation of the integer prog ...
Improving Reinforcement Learning by using Case Based
Improving Reinforcement Learning by using Case Based

... Although several methods have been successfully applied for defining the heuristic function, a very interesting option has not been explored yet: the reuse of previously learned policies, using a Case Based Reasoning approach to define an heuristic function. This paper investigates the combination o ...
singularity hypotheses
singularity hypotheses

... the singularity is a religious notion, not a scientific one (Horgan 2008; Proudfoot this volume; Bringsjord et al. this volume). Other critics (Chaisson this volume) accept acceleration as an underlying law of nature but claim that, in perspective, the significance of the claimed changes is overblow ...
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... AI in chess playing – Deep Blue (1997) ...
Thinking Machines
Thinking Machines

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Oral History Interview with Patrick H. Winston
Oral History Interview with Patrick H. Winston

... is, what was the interaction inside the laboratory then? For example, there is a large space out there, outside your office which looks like many people could congregate there at any given time in the day, maybe at coffee hour in the morning or something. ...
$doc.title

... To  be  arranged.   ...
From Agent Theory to Agent Construction: A Case Study
From Agent Theory to Agent Construction: A Case Study

... and practice in two ways. First, he provided an abstract agent architecture that serves as an idealization of an implemented system and as a means for investigating theoretical properties [27]. A second effort developed an alternative formalization by starting with an implemented system and then for ...
From Agent Theory to Agent Construction: A Case Study
From Agent Theory to Agent Construction: A Case Study

Modernizing Machine-to-Machine Interactions
Modernizing Machine-to-Machine Interactions

... industrial devices that autonomously connect to the Industrial Internet, execute native or cloud-based machine apps, and analyze collected data and react to changes in those data. They are predictive (anticipating and reacting to state changes), reactive (sensing the environment and acting on it), a ...
Plan Synthesis for Knowledge and Action Bases - CEUR
Plan Synthesis for Knowledge and Action Bases - CEUR

... our examples, we use the standard DL ALCQIH [2]. We also consider lightweight DLs of the DL-Lite family, in particular DL-LiteA [9], for which both checking KB satisfiability and answering ECQs over a KB are FO-rewritable [10, 9]. The latter means that every ECQ Q expressed over a DL-LiteA TBox T ca ...
PDF file
PDF file

... considered very difficult (by humans), such as playing simulated chess games. However, they have done poorly in areas that are commonly considered easy for humans, such as vision, audition, and natural language understanding. On the other hand, there is a lack of appreciation of what the human mind ...
Decision Support Systems - University of Pittsburgh
Decision Support Systems - University of Pittsburgh

Viability of Artificial Neural Networks in Mobile Health- care Gavin Harper
Viability of Artificial Neural Networks in Mobile Health- care Gavin Harper

... seminal paper, Computing Machinery and Intelligence. In this paper, Turing proposed a test called the “imitation game” (Turing 1950) though this has been since renamed the Turing Test, that could determine the relative intelligence of a machine by prompting the machine and analysing the responses. T ...
2 IX Approach - Artificial Intelligence Applications Institute
2 IX Approach - Artificial Intelligence Applications Institute

... to create and interact with a "dynamic event list" to assist with the monitoring of execution outcomes and the resultant actions / changes / new taskings. Links can be created between related tasks (by the user or inferred by the system) and the system can monitor dependencies, etc. The process pane ...
IP2 - IX
IP2 - IX

... to create and interact with a "dynamic event list" to assist with the monitoring of execution outcomes and the resultant actions / changes / new taskings. Links can be created between related tasks (by the user or inferred by the system) and the system can monitor dependencies, etc. The process pane ...
Exploiting Bounds in Operations Research and Artificial Intelligence
Exploiting Bounds in Operations Research and Artificial Intelligence

... worked on an abstract search space. Problem abstraction is a general method for developing evaluation functions, and has been studied and applied to planning and combinatorial games and puzzles as well as many other AI problems [23, 39]. The goal here is to construct an abstraction, or a simplified ...
Lecture 11 - Chapter 7
Lecture 11 - Chapter 7

... Other Artificial Intelligence Applications • Genetic algorithm – An approach to solving complex problems in which a number of related operations or models change and evolve until the best one emerges ...
artificial intelligence and the future of defense
artificial intelligence and the future of defense

No Slide Title
No Slide Title

... -because of weight symmetry, anti-patterns (binary reverse) are stored as well as the original patterns (also spurious local minima are created when many patterns are stored) -if one tries to store more than about 0.14*(number of neurons) patterns, the network exhibits unstable behavior - works well ...
Fuzzy Expert Control Systems: Knowledge Base Validation
Fuzzy Expert Control Systems: Knowledge Base Validation

... practical systems with unknown models, apart from basic qualitative characteristics, these techniques cannot be applied with total confidence. Paradoxically, fuzzy expert control was born to control precisely these systems. So, the conclusion is that additional problems arise in order to create effi ...
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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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