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A proposal of a novel model for Artificial Intelligence Planning
A proposal of a novel model for Artificial Intelligence Planning

... [ISSN 2250 – 3749] Publication Date : 05 June 2013 [2] Rolf Pfeifer and Gabriel Gome‖Interacting with the real world – design principles for intelligent systems‖. [3] Rodney A. Brooks “Intelligence without representation*”. [4] Progress in AI Planning Research and Applications, Derek Long and Maria ...
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...  The main strengths Asia has to become a leader in AI adoption is its vast and emerging talent pool, a freedom from legacy assets and the massive amount of data that is being collected across the region. However, Asia still lags developed markets like the US and the UK in terms of innovation and ha ...
Karlsruhe Text - Tecfa
Karlsruhe Text - Tecfa

The role of artificial intelligence techniques in training
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... 1. The major contribution of AI to educational and training software is the possibility to model expertise. This expertise is the main feature of AI-based courseware: the system is able to solve the problems that the learner has to solve. The system is knowledgeable in the domain to be taught. Of c ...
Expressive AI
Expressive AI

... language use, etc.). These mental components duplicate the capabilities of high-level human reasoning in abstract, ...
Cognitive architectures
Cognitive architectures

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On John McCarthy`s 80th Birthday, in Honor of his Contributions
On John McCarthy`s 80th Birthday, in Honor of his Contributions

... Fifty years ago, John McCarthy embarked on a bold and unique plan to achieve human-level intelligence in computers. It was not his dream of an intelligent computer that was unique, or even first: Alan Turing (Turing 1950) had envisioned a computer that could converse intelligently with humans back i ...
Reasoned Use of Expertise in Argumentation
Reasoned Use of Expertise in Argumentation

... shown how conditions on reasoning can be set that eliminate circular argumentation as "fallacious." However, it is argued in Walton and Batten that circular reasoning is not necessarily fallacious in all cases, but only subject to criticism in certain contexts of reasoning. Two farther characteristi ...
Building Intelligent Tutoring Systems: An Overview
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AI Intro slides - Cornell Computer Science
AI Intro slides - Cornell Computer Science

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A Multi-intelligent Agent System for Automatic Construction of Rule
A Multi-intelligent Agent System for Automatic Construction of Rule

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Artificial Intelligence for Speech Recognition
Artificial Intelligence for Speech Recognition

... Intonation and sentence stress can play an important role in the interpretation of an utterance. As a simple example, utterances that might be transcribed as "go!", "go?" and "go." can clearly be recognized by a human, but determining which intonation corresponds to which punctuation is difficult fo ...
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... To help increase the size and diversity of the computing community engaged in environmental and societal sustainability, we have initiated a lab text entitled Artificial Intel ligence for Computational Sustainability: A Lab Compan ion (AISustBook, 2012) as a resource for undergraduate AI courses. We ...
Uluslararası İnsan Bilimleri Dergisi
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... 6. Learning with PDAs and IPAs Developed on a spoken dialogue system that uses a natural spoken language and semantic understanding techniques in an attempt to help the users obtain desired information (Chen, 2015), IPAs could be used for self-learning purposes. As also indicated in Horizon Report b ...
Soft Computing and its Applications
Soft Computing and its Applications

... solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neuron ...
Artificial Intelligence: Overview
Artificial Intelligence: Overview

... The dream of creating artificial devices that reach or outperform human intelligence is an old one, but a computationally efficient theory of true intelligence has not been found yet, despite considerable efforts in the last 50 years. Nowadays most research is more modest, focussing on solving more ...
Applications of Automated Reasoning Nr. 9/2007 Arbeitsberichte
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... by the Automated Reasoner EQP for first order equational logic, developed at Argonne National Laboratory [McC97]. Propositional reasoning systems are very successful in soft- and hardware verification, where the length of formulae which can be processed has grown by orders of magnitude over the last ...
Frontiers: Exploring the Digital Future of Management
Frontiers: Exploring the Digital Future of Management

... spond to rapid technological innovations. Looking back, way they run their everyday life rather than on their productivity I made four predictions about management and technology. at work. Inevitably, this will change in the next five years — but First, it was clear to me that the manager’s role as ...
DCP 1172: Introduction to Artificial Intelligence
DCP 1172: Introduction to Artificial Intelligence

... 01-Introduction. [AIMA Ch 1] Course Schedule. Homeworks, exams and grading. Course material, TAs and office hours. Why study AI? What is AI? The Turing test. Rationality. Branches of AI. Research disciplines connected to and at the foundation of AI. Brief history of AI. Challenges for the future. Ov ...
Keonwook Kim - Mercer University
Keonwook Kim - Mercer University

Artificial Intelligence and Expert Systems
Artificial Intelligence and Expert Systems

... Expert Systems Versus Knowledge-based Systems Rule-based Expert Systems Frame-based Systems Hybrid Systems Model-based Systems Ready-made (Off-the-Shelf) Systems Real-time Expert Systems ...
Genetic algorithms approach to feature discretization in artificial
Genetic algorithms approach to feature discretization in artificial

... of categories to be discretized using these bits. The thresholds are not used if the searched thresholds are more than the maximum value of each feature. The upper limit of the number of categories is five and the lower limit is one. This number is automatically determined by the searching process o ...
Turing`s thinking machines: resonances with
Turing`s thinking machines: resonances with

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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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