15-01-2015, 04:46 PM
I' have a course on AI, so i need the book to study about AI,
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artificial intelligence and intelligent systems by np padhy pdf free download
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I' have a course on AI, so i need the book to study about AI,
13-04-2017, 12:40 PM
The original vision of AI was to develop intelligent artifacts with the same wide range of capabilities that we observe in humans. None of the early programs in the field achieved this high objective, but the tone of many seminal documents makes this motivation very clear. When we entered graduate school at Carnegie Mellon University in the mid-1970s, this goal was a central tenet of many researchers in the field, and was still important in many circles when we became teachers in the mid-1980s. At that time AI was still generally seen as a single field with a common set of goals.
By the end of the 1980s, that situation had begun to change. Subfields such as automatic learning, knowledge representation and planning began to separate from AI, establishing their own lectures, journals and criteria for progress. One of us was an active promoter of such developments in the area of automatic learning, which launched one of the first specialized journals and played a leading role in the introduction of a careful experimental evaluation. For researchers who were involved in these movements, these changes seemed necessary at the time of advancing the parental field.
However, the downside of this speciation was that students began to identify more with their subfield than with AI in general. They began to focus their energies on solving component problems, such as supervised learning or constraint satisfaction, with little concern as to how their results could be used in the context of larger AI systems. In the last 20 years, this trend has continued unabated. Clearly, it has produced considerable technical progress within each of AI's subfields, but it has also led to narrow-mindedness among many other excellent researchers.
Today, many AI practitioners consider that their primary affiliation is not with the AI itself but with its subfield, and its primary conference is not AAAI but one of the specialized meetings. In many cases, they lack the necessary training to understand the results in other areas or even to appreciate their objectives, an effect that is exacerbated by the specialized jargon that has emerged. In fact, postgraduate education in AI subfields has become so specialized that the only common knowledge concerns the complexity of goritics. This trend has reached its most complete development in the new machine learning department of Carnegie Mellon, which requires its graduate students to take many kinds of statistics, but not a single course of artificial intelligence.
This change in affiliation has been accompanied by a change in the level of problems addressed. Each subfield has defined its own set of well-defined tasks in which progress is measured. For example, automatic learning now deals almost exclusively with supervised learning for grading and learning delayed reward for reactive control, with other subfields having similar problems. Despite their advantages for evaluation purposes and, in some cases, their practical usefulness (for example, for corporate data mining), they tell us little about the nature of intelligence.
Computer science departments, which have become the typical home of AI research, are often divided into areas such as systems and algorithms. When we entered the field, most of the AI researchers we knew would have said that they were more like system people than people of algorithms, whereas today most would take exactly the opposite position. This reflects the current concern with the mechanisms of the components rather than with the way they work together to support intelligent behavior.
Despite these developments, there have been some important trends. In particular, research on cognitive architectures (Newell, 1990, Langley et al., In press) addresses accounts at the level of intelligent behavior systems. Launched in the late 1970s, this paradigm has continued through the present and produced some impressive examples of intelligent agents operating in complex environments. For example, Tambe et al. (1995) report a simulated combat pilot, implemented within the framework of Soar, which incorporates substantial knowledge about flight missions and has been used repeatedly in large-scale military training drills. Similarly, Trafton et al.
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