Despite this, the traditional textbooks continue to expect mathematical and programming expertise beyond the scope of current undergraduates and focus on areas not relevant to many of todays courses.Negnevitsky shows students how to build intelligent systems drawing on techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation and now also intelligent agents.The principles behind these techniques are explained without resorting to complex mathematics, showing how the various techniques are implemented, when they are useful and when they are not.
Artificial Intelligence Guide Intelligent Systems 2Nd Edition Software Tools AvailableNo particular programming language is assumed and the book does not tie itself to any of the software tools available.
However, available tools and their uses will be described and program examples will be given in Java. The lack of assumed prior knowledge makes this book ideal for any introductory courses in artificial intelligence or intelligent systems design, while the contemporary coverage means more advanced students will benefit by discovering the latest state-of-the-art techniques. Accompanying website including student projects, accompanying software tools, software demonstrations, PowerPoints and solutions to exercises. Linked coverage of all the latest artificial intelligence topics. In many cases, they lack the training to understand results in other areas or even to appreciate their goa ls, a n e ect that is exacerbated b y the specia lized jargons that hav e emerged. Artificial Intelligence Guide Intelligent Systems 2Nd Edition Download Citation CopyLaird University of Michigan Download full-text PDF Read full-text Download full-text PDF Read full-text Download citation Copy link Link copied Read full-text Download citation Copy link Link copied Citations (16) References (9) Abstract The original vision of AI was to develop intelligent ar- tifacts with the same broad range of capabilities as we observe in humans. None of the fields early programs achieved this lofty goal, but the tone of many seminal papers makes this motivation very clear. When we en- tered graduate school at Carnegie Mellon University in the mid 1970s, this aim was a central tenet of many re- searchers in the field, and it was still important in many circles when we became professors in the mid 1980s. At that time, AI was still generally viewed as a single field with a common set of goals. Subfields like machine learning, knowledge rep- resentation, and planning began to break away from AI, establishing their own conferences, journals, and crite- ria for progress. One of us served as an active proponent of such developments in the area of machine learning, which launched one of the first specialized journals and which played a leading role in introducing careful exper- imental evaluation. To researchers who were involved in these movements, these changes seemed necessary at the time for advancing the parent field. However, the down side 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, like supervised learn- ing or constraint satisfaction, with little concern for how their results might be used in the context of larger AI systems. Over the past 20 years, this trend has con- tinued unabated. Clearly, it has produced considerable technical progress within each of AIs subfields, but it has also led to a narrowness of vision among many oth- erwise excellent researchers. Today, many AI practitioners consider their main af- filiation to be not with AI itself but with their subfield, and their primary conference is not AAAI but one of the specialized meetings. In many cases, they lack the training to understand results in other areas or even to appreciate their goals, an effect that is exacerbated by the specialized jargons that have emerged. Artificial Intelligence Guide Intelligent Systems 2Nd Edition For Free Public FullIn fact, graduate education in AI subfields has become so spe- cialized that the only common knowledge concerns al- Copyright c 2006, American Association for Artificial In- Discover the worlds research 20 million members 135 million publications 700k research projects Join for free Public Full-text 1 Content uploaded by John E. Laird Author content All content in this area was uploaded by John E. None of the elds e a rly pr ograms achiev ed this lo ft y go al, but the tone of many seminal pap ers makes t his motiv ation v er y clear. When w e en- tered graduate school at Carnegie Mellon Universit y in the mid 1970s, this aim w as a central tenet o f many re- searchers in the eld, and it w as still important in many circles when we became professor s in the mid 1980s. A t that time, AI was still generally viewed as a sing le eld with a common set of goals. Subelds like machine lea rning, kno wledge rep- resentation, a nd pla nning b ega n to br eak awa y from AI, establishing their own conferences, journals, and crite- ria for progr ess. O ne of us served as an active prop onent of such developmen ts in the area of machine lear ning, which launc hed one of the rst sp ecialized journals and which play ed a lea ding ro le in introducing careful ex per - imen tal ev aluation. T o resea rchers who were inv olved in these mov ements, these c hanges seemed necessa r y at the time for adv ancing the paren t eld. How ever, the down side of this s p eciatio n was that studen ts began to iden tify more with their s ubeld than with AI in g eneral. They beg an to fo cus their energies on solving comp onent problems, like supervis ed learn- ing or constraint satisfaction, with little concern for how their results migh t b e used in the context of larger AI systems. Over the past 20 years, this trend has con- tin ued unabated. Clearly, it has pro duced considerable tech nical progress within each of AIs subelds, but it has also led to a narr owness of vision a mo ng man y oth- erwise excellent rese a rchers. ![]()
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