chapter1.ps

收集了遗传算法、进化计算、神经网络、模糊系统、人工生命、复杂适应系统等相关领域近期的参考论文和研究报告

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612 792 0 FMBEGINPAGE
108 72 540 81 R
7 X
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V
0 12 Q
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(1) 324 73 T
108 90 540 648 R
7 X
V
0 X
(CHAPTER I) 293.18 640 T
(INTRODUCTION: SEARCH AND EXPLICIT KNOWLEDGE) 169.44 604 T
1 F
(1.0  Intr) 108 562 T
(oduction: Sear) 149.43 562 T
(ch and Explicit Knowledge) 224.17 562 T
0 F
-0.24 (T) 126 536 P
-0.24 (wo broad goals cover most of the subtopics within arti\336cial intelligence \050AI\051 research) 132.49 536 P
-0 (\050Schank 1987\051. One goal is the study of human intelligence and problem solving by creat-) 108 518 P
0.99 (ing computational models \050e.g. Newell and Simon 1963; Anderson 1976, 1983; Minsky) 108 500 P
0.33 (1985; McClelland and Rumelhart 1986a; Newell 1990; and many others\051. Such cognitive) 108 482 P
0.03 (modeling work is concerned mainly with issues of human psychological performance, but) 108 464 P
0.83 (not necessarily making programs intelligent, although this is occasionally assumed. AI\325) 108 446 P
0.83 (s) 535.33 446 P
0.02 (alternative goal is the creation of techniques that allow computers to perform well at tasks) 108 428 P
1.65 (that humans are currently more pro\336cient \050e.g. Samuel 1959; Shortlif) 108 410 P
1.65 (fe 1976; Berliner) 454.76 410 P
0.31 (1977; Lindsay et al 1980; Erman et al. 1980; and many others\051. Like its cognitive model-) 108 392 P
2.42 (ing alter) 108 374 P
2.42 (-ego, this AI methodology often involves considering human intelligence for) 149.82 374 P
1.34 (computational inspiration. However) 108 356 P
1.34 (, it is not necessary to recapitulate human cognitive) 283.09 356 P
(abilities for a computer to perform intelligently) 108 338 T
(.) 333.74 338 T
1.2 (A commonality in both forms of AI is) 126 308 P
2 F
1.2 (sear) 320.84 308 P
1.2 (ch) 341.05 308 P
0 F
1.2 (. Search is the topic from which most) 352.37 308 P
0.82 (other AI subtopics specialize, including learning, discovery) 108 290 P
0.82 (, pattern recognition, classi\336-) 397.29 290 P
0.72 (cation, diagnosis and design. Search methods for such problems fall into two categories,) 108 272 P
-0.15 (strong and weak.) 108 254 P
2 F
-0.15 (Str) 192.16 254 P
-0.15 (ong methods) 205.72 254 P
0 F
-0.15 ( are rich in task-speci\336c knowledge that a programmer or) 266.53 254 P
0.51 (knowledge engineer places explicitly into the search mechanism. Strong methods tend to) 108 236 P
1.19 (be narrowly focused but fairly ef) 108 218 P
1.19 (\336cient in their ability to identify domain speci\336c solu-) 271.28 218 P
0.56 (tions.) 108 200 P
2 F
0.56 (W) 137.88 200 P
0.56 (eak methods) 146.77 200 P
0 F
0.56 ( encode search strategies that are task independent and consequently) 206.94 200 P
0.96 (less ef) 108 182 P
0.96 (\336cient. Strong methods often use one or more weak methods working underneath) 139.05 182 P
0.31 (the task-speci\336c knowledge. In spite of the central role of search in AI, the focus of AI is) 108 164 P
0.35 (to solve problems. Given this fact, this dissertation often refers to a particular search task) 108 146 P
0.56 (as an instance of) 108 128 P
2 F
0.56 (pr) 192.84 128 P
0.56 (oblem solving) 203.06 128 P
0 F
0.56 (. Likewise, an algorithm that performs problem solving) 270.58 128 P
(is referred to as a) 108 110 T
2 F
(pr) 193.59 110 T
(oblem solver) 203.8 110 T
0 F
(.) 264.77 110 T
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612 792 0 FMBEGINPAGE
108 63 540 702 R
7 X
0 K
V
108 711 540 720 R
V
0 12 Q
0 X
(2) 534 712 T
108 90 540 702 R
7 X
V
0 X
0.09 (Often a task is too dif) 126 694 P
0.09 (\336cult to engineer out suf) 229.84 694 P
0.09 (\336cient knowledge for its solution, or the) 347.24 694 P
1.21 (goal is to discover solutions too complex for humans to construct. In these cases, there) 108 676 P
0.31 (may be no strong method to rely on and the augmentation of a weak method by task-spe-) 108 658 P
0.75 (ci\336c knowledge is infeasible. Such complex problems still \336t well within the AI charter) 108 640 P
0.75 (,) 537 640 P
0.18 (but by virtue of their complexity traditional AI techniques can not adequately solve them.) 108 622 P
1.93 (Thus, the strong vs. weak dichotomy presents a constrained methodology for creating) 108 604 P
2 (intelligent programs in AI; either explicitly engineer out task knowledge or analytical) 108 586 P
(solutions for a strong method or be doomed to the inef) 108 568 T
(\336cient search of weak methods.) 368.95 568 T
0.04 (The focus of the following work is twofold, as re\337ected in the title of this dissertation.) 126 538 P
1.39 (The primary focus is to investigate algorithms that do not require) 108 520 P
2 F
1.39 (explicitly engineer) 438.8 520 P
1.39 (ed) 528.68 520 P
1.14 (task knowledge) 108 502 P
0 F
1.14 ( for search but still perform as strong methods. These methods \336t into a) 182.75 502 P
1.64 (class of techniques that this dissertation de\336nes as) 108 484 P
2 F
1.64 (emer) 364.32 484 P
1.64 (gent intelligence) 387.85 484 P
0 F
1.64 (. Of particular) 469.11 484 P
0.62 (interest is the investigation of emer) 108 466 P
0.62 (gent intelligence using evolutionary algorithms, algo-) 279.75 466 P
0.12 (rithms that use an analogy to natural evolution as their base computational engine. Before) 108 448 P
0.59 (discussing these points and the content of this dissertation further) 108 430 P
0.59 (, the following sections) 426.3 430 P
1.86 (discuss the approach of knowledge-based arti\336cial intelligence, its reliance on explicit) 108 412 P
(task knowledge and the classic problems which result.) 108 394 T
1 F
(1.1  Literal Knowledge Level Models) 108 358 T
0 F
0.05 (Newell \0501981, 1982\051 de\336nes two levels of observation that can be used to describe the) 126 334 P
1.86 (intelligence contained by any agent, including a computer) 108 316 P
1.86 (. The \336rst, the) 398.14 316 P
2 F
1.86 (symbol level) 475.52 316 P
0 F
1.86 (,) 537 316 P
0.03 (describes the representations and the program used by the agent. This level roughly corre-) 108 298 P
0.2 (sponds with Marr) 108 280 P
0.2 (\325) 193.46 280 P
0.2 (s \0501981, 1982\051 representation and algorithm level. The) 196.79 280 P
2 F
0.2 (knowledge level) 462.86 280 P
0 F
-0.13 (is a level of description above the symbol level. A description of an intelligent agent at the) 108 262 P
0.12 (knowledge level has four components. The \336rst is a set of) 108 244 P
2 F
0.12 (actions) 389.16 244 P
0 F
0.12 ( that can be executed by) 423.81 244 P
2.08 (the agent. Second is a collection of) 108 226 P
2 F
2.08 (goals) 293.11 226 P
0 F
2.08 ( for the agent that provide a context for its) 319.1 226 P
2.02 (actions. The third component is) 108 208 P
2 F
2.02 (knowledge) 272.68 208 P
0 F
2.02 ( that allows the agent to achieve the goals) 323.97 208 P
-0.07 (through the execution of actions. The \336nal component is what Newell \0501981, 1982\051 calls a) 108 190 P
2 F
0.17 (principle of rationality) 108 172 P
0.17 (.) 216.97 172 P
0 F