{"id":77,"date":"2020-02-23T11:17:52","date_gmt":"2020-02-23T11:17:52","guid":{"rendered":"https:\/\/alandix.com\/aibook\/?page_id=77"},"modified":"2020-03-29T15:31:44","modified_gmt":"2020-03-29T15:31:44","slug":"resources-and-detailed-contents","status":"publish","type":"page","link":"https:\/\/alandix.com\/aibook\/first-edition\/resources-and-detailed-contents\/","title":{"rendered":"resources and detailed contents"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\" id=\"intro\">Introduction<\/h2>\n\n\n\n<dl> \n  <dt> What is artificial intelligence? 1 <\/dt> \n  <dt> History of artificial intelligence 3  <\/dt> \n  <dd> <ul><li> You can see more about <a href=\"http:\/\/hci.stanford.edu\/~winograd\/shrdlu\/\" target=\"_blank\" rel=\"noopener noreferrer\">SHRDLU<\/a> on <a href=\"http:\/\/hci.stanford.edu\/%7Ewinograd\/\" target=\"_blank\" rel=\"noopener noreferrer\">Terry \n    Winnograd<\/a>&#8216;s own HCI course pages\n\t<\/li>\n  <li> And an online web version of  <a href=\"http:\/\/www-ai.ijs.si\/eliza\/\" target=\"_blank\" rel=\"noopener noreferrer\">Eliza<\/a> you can talk to.\n<\/li> <\/ul>\n  <\/dd>\n\n  <dt>The future for AI 7  <\/dt> \n<\/dl>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ch1\">1&nbsp;&nbsp;Knowledge in AI<\/h2>\n\n\n\n<dl>\n  <dt>  1.1\tOverview\t9 <\/dt>\n\n  <dt>  1.2\tIntroduction\t9 <\/dt>\n\n  <dt>  1.3\tRepresenting knowledge\t10 <\/dt>\n\n  <dt>  1.4\tMetrics for assessing knowledge representation schemes\t13 <\/dt>\n\n  <dt>  1.5\tLogic representations\t14 <\/dt>\n  <dd> <ul><li>  <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch1\/tbirds.p\" target=\"_blank\" rel=\"noopener noreferrer\">tbirds.p<\/a> (Prolog) <\/li>\n <li> you may have noticed that the characters in this example come from <a href=\"https:\/\/en.wikipedia.org\/wiki\/Thunderbirds_(TV_series)\" target=\"_blank\" rel=\"noopener noreferrer\">Thunderbirds<\/a>,\n         the classic <a href=\"https:\/\/en.wikipedia.org\/wiki\/Gerry_Anderson\" target=\"_blank\" rel=\"noopener noreferrer\">Gerry Anderson<\/a> television series. <\/li><\/ul><\/dd>\n\n  <dt>  1.6\tProcedural representation\t17 <\/dt>\n  <dd><dl>\n      <dt>  1.6.1\tThe database\t17 <\/dt>\n\n      <dt>  1.6.2\tThe production rules\t18 <\/dt>\n\n      <dt>  1.6.3\tThe interpreter\t18 <\/dt>\n\n      <dt>  1.6.4\tAn example production system: making a loan\t19 <\/dt>\n    <dd> <ul><li> <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch1\/prod.p\" target=\"_blank\" rel=\"noopener noreferrer\">prod.p<\/a> (Prolog) <\/li><\/ul><\/dd>\n    <\/dl> <\/dd>\n\n  <dt>  1.7\tNetwork representations\t21 <\/dt>\n  <dd> <ul><li> <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch1\/network.p\" target=\"_blank\" rel=\"noopener noreferrer\">network.p<\/a> (Prolog) <\/li><\/ul><\/dd>\n\n  <dt>  1.8\tStructured representations\t23 <\/dt>\n  <dd><dl>\n      <dt>  1.8.1\tFrames\t23 <\/dt>\n    <dd> <ul><li> <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch1\/frames.p\" target=\"_blank\" rel=\"noopener noreferrer\">frames.p<\/a> (Prolog) <\/li><\/ul><\/dd>\n\n      <dt>  1.8.2\tScripts\t24 <\/dt> \n    <\/dl><\/dd>\n\n  <dt>  1.9\tGeneral knowledge\t26 <\/dt>\n\n  <dt>  1.10\tThe frame problem\t27 <\/dt>\n\n  <dt>  1.11\tKnowledge elicitation\t28 <\/dt>\n\n  <dt>  1.12\tSummary\t28 <\/dt>\n\n  <dt>  1.13\tExercises\t29 <\/dt>\n\n  <dt>  1.14\tRecommended further reading\t30 <\/dt>\n<\/dl>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ch2\">2&nbsp;&nbsp;Reasoning<\/h2>\n\n\n\n<dl>\n  <dt>  2.1\tOverview\t31 <\/dt>\n\n  <dt>  2.2\tWhat is reasoning?\t31 <\/dt>\n\n  <dt>  2.3\tForward and backward reasoning\t33 <\/dt>\n  <dd> <ul><li> <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch2\/forback.p\" target=\"_blank\" rel=\"noopener noreferrer\">forback.p<\/a> (Prolog)<\/li><\/ul> <\/dd>\n\n  <dt>  2.4\tReasoning with uncertainty\t34 <\/dt>\n  <dd><dl>\n      <dt>  2.4.1\tNon-monotonic reasoning\t35 <\/dt>\n    <dd> <ul><li> <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch2\/tms.p\" target=\"_blank\" rel=\"noopener noreferrer\">tms.p<\/a> (Prolog: TMS &#8211; truth maintenance system) <\/li><\/ul><\/dd>\n\n      <dt>  2.4.2\tProbabilistic reasoning\t36 <\/dt>\n    <dd> <ul><li> <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch2\/bayes.p\" target=\"_blank\" rel=\"noopener noreferrer\">bayes.p<\/a> (Prolog: bayesian reasoning) <\/li><\/ul><\/dd>\n\n      <dt>  2.4.3\tCertainty factors\t37 <\/dt>\n    <dd> <ul><li> <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch2\/certf.p\" target=\"_blank\" rel=\"noopener noreferrer\">certf.p<\/a> (Prolog) <\/li><\/ul><\/dd>\n\n      <dt>  2.4.4\tFuzzy reasoning\t39 <\/dt>\n    <dd> <ul><li> <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch2\/fuzzy.p\" target=\"_blank\" rel=\"noopener noreferrer\">fuzzy.p<\/a> (Prolog) <\/li><\/ul><\/dd>\n\n      <dt>  2.4.5\tReasoning by analogy\t40 <\/dt>\n\n      <dt>  2.4.6\tCase-based reasoning\t40 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  2.5\tSummary\t43 <\/dt>\n\n  <dt>  2.6\tExercises\t43 <\/dt>\n\n  <dt>  2.7\tRecommended further reading\t44 <\/dt>\n<\/dl>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ch3\">3&nbsp;&nbsp;Search<\/h2>\n\n\n\n<dl>\n  <dt>  3.1\tIntroduction\t45 <\/dt>\n  <dd><dl>\n      <dt>  3.1.1\tTypes of problem\t45 <\/dt>\n\n      <dt>  3.1.2\tStructuring the search space\t49 <\/dt>\n      <dd> <ul><li> <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch3\/gentest.p\" target=\"_blank\" rel=\"noopener noreferrer\">gentest.p<\/a> (Prolog) generate and test for magic squares <\/li>\n      <li> <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch3\/prune.p\" target=\"_blank\" rel=\"noopener noreferrer\">prune.p<\/a> (Prolog) pruning partial magic square solutions <\/li>\n      <li> <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch3\/hanext.p\" target=\"_blank\" rel=\"noopener noreferrer\">hanext.p<\/a> (Prolog) representation of Towers of Hanoi graph <\/li>\n      <li> <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch3\/hanint.p\" target=\"_blank\" rel=\"noopener noreferrer\">hanint.p<\/a> (Prolog) alternative representation of Towers of Hanoi graph  <\/li>\n      <li> See also <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch3\/proof.p\" target=\"_blank\" rel=\"noopener noreferrer\">proof.p<\/a> (Prolog) at 3.2.6 for addition graph  <\/li><\/ul> <\/dd>\n    <\/dl> <\/dd>\n\n  <dt>  3.2\tExhaustive search and simple pruning\t55 <\/dt>\n  <dd><dl>\n      <dt>  3.2.1\tDepth and breadth first search\t55 <\/dt>\n\n      <dt>  3.2.2\tComparing depth and breadth first searches\t58 <\/dt>\n\n      <dt>  3.2.3\tProgramming and space costs\t59 <\/dt>\n\n      <dt>  3.2.4\tIterative deepening and broadening\t60 <\/dt>\n\n      <dt>  3.2.5\tFinding the best solution &#8212; branch and bound\t61 <\/dt>\n\n      <dt>  3.2.6\tGraph search\t62 <\/dt>\n      <dd> <ul><li> <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch3\/proof.p\" target=\"_blank\" rel=\"noopener noreferrer\">proof.p<\/a> (Prolog) arithmetic proof using closed lists <\/li><\/ul><\/dd>\n    <\/dl> <\/dd>\n\n  <dt>  3.3\tHeuristic search\t63 <\/dt>\n  <dd><dl>\n      <dt>  3.3.1\tHill climbing and best first &#8212; goal-directed search\t64 <\/dt>\n\n      <dt>  3.3.2\tFinding the best solution &#8212; the A*\t algorithm\t65 <\/dt>\n\n      <dt>  3.3.3\tInexact search\t68 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  3.4\tKnowledge-rich search\t71 <\/dt>\n  <dd><dl>\n      <dt>  3.4.1\tConstraint satisfaction\t72 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  3.5\tSummary\t74 <\/dt>\n\n  <dt>  3.6\tExercises\t75 <\/dt>\n\n  <dt>  3.7\tRecommended further reading\t75 <\/dt>\n<\/dl>\n\n\n\n<h2 class=\"wp-block-heading\">4&nbsp;&nbsp;Machine learning<\/h2>\n\n\n\n<dl>\n  <dt>  4.1\tOverview\t77 <\/dt>\n\n  <dt>  4.2\tWhy do we want machine learning?\t77 <\/dt>\n\n  <dt>  4.3\tHow machines learn\t78 <\/dt>\n  <dd><dl>\n      <dt>  4.3.1\tPhases of machine learning\t79 <\/dt>\n\n      <dt>  4.3.2\tRote learning and the importance of generalization\t80 <\/dt>\n\n      <dt>  4.3.3\tInputs to training\t81 <\/dt>\n\n      <dt>  4.3.4\tOutputs of training\t82 <\/dt>\n\n      <dt>  4.3.5\tThe training process\t83 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  4.4\tDeductive learning\t84 <\/dt>\n\n  <dt>  4.5\tInductive learning\t85 <\/dt>\n  <dd><dl>\n      <dt>  4.5.1\tVersion spaces\t86 <\/dt>\n      <dd>  <span class=\"warning-icon\"><\/span>  page 88, <a href=\"https:\/\/alandix.com\/aibook\/first-edition\/errata\/#e1\">errata<\/a> 5\/11\/97 <\/dd>\n\n      <dt>  4.5.2\tID3 and decision trees\t90 <\/dt>\n      <dd>  <ul><li> additional <a href=\"QBB\/id3.html\" target=\"_blank\" rel=\"noopener noreferrer\">notes and pseudo-code<\/a> for ID3 <\/li>\n      <li> also <a href=\"QBB\/var.html\" target=\"_blank\" rel=\"noopener noreferrer\">notes<\/a> on extending ID3 to cope with between variable comparisons <\/li><\/ul><\/dd>\n\n      <dt>  4.5.3\tRule induction\t95 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  4.6\tExplanation-based learning\t96 <\/dt>\n\n  <dt>  4.7\tExample: Query-by-Browsing\t97 <\/dt>\n  <dd><dl>\n      <dt>  4.7.1\tWhat the user sees\t97 <\/dt>\n\n      <dt>  4.7.2\tHow it works\t99 <\/dt>\n\n      <dt>  4.7.3\tProblems\t99 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  4.8\tSummary\t99 <\/dt>\n\n  <dt>  4.9\tRecommended further reading\t100 <\/dt>\n<\/dl>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ch5\">5&nbsp;&nbsp;Game playing<\/h2>\n\n\n\n<dl>\n  <dt>  5.1\tOverview\t101 <\/dt>\n\n  <dt>  5.2\tIntroduction\t101 <\/dt>\n\n  <dt>  5.3\tCharacteristics of game playing\t103 <\/dt>\n\n  <dt>  5.4\tStandard games\t104 <\/dt>\n  <dd><dl>\n      <dt>  5.4.1\tA simple game tree\t104 <\/dt>\n\n      <dt>  5.4.2\tHeuristics and minimax search\t105 <\/dt>\n\n      <dt>  5.4.3\tHorizon problems\t107 <\/dt>\n\n      <dt>  5.4.4\tAlpha&#8211;beta pruning\t108 <\/dt>\n\n      <dt>  5.4.5\tThe imperfect opponent\t109 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  5.5\tNon-zero-sum games and simultaneous play\t109 <\/dt>\n  <dd><dl>\n      <dt>  5.5.1\tThe prisoner&#8217;s dilemma\t110 <\/dt>\n\n      <dt>  5.5.2\tSearching the game tree\t111 <\/dt>\n\n      <dt>  5.5.3\tNo alpha&#8211;beta pruning\t112 <\/dt>\n\n      <dt>  5.5.4\tPareto-optimality\t113 <\/dt>\n\n      <dt>  5.5.5\tMulti-party competition and co-operation\t114 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  5.6\tThe adversary is life!\t114 <\/dt>\n  <dd>  <span class=\"warning-icon\"><\/span> page 115, <a href=\"https:\/\/alandix.com\/aibook\/first-edition\/errata\/#e2\">errata<\/a> 10\/1\/2000 <\/dd>\n\n\n  <dt>  5.7\tProbability\t116 <\/dt>\n  <dd>  <span class=\"warning-icon\"><\/span> page 117, <a href=\"https:\/\/alandix.com\/aibook\/first-edition\/errata\/#e3\" target=\"_blank\" rel=\"noopener noreferrer\">errata<\/a> 10\/1\/2000 <\/dd>\n\n  <dt>  5.8\tSummary\t117 <\/dt>\n\n  <dt>  5.9\tExercises\t118 <\/dt>\n\n  <dt>  5.10\tRecommended further reading\t119 <\/dt>\n<\/dl>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ch6\">6&nbsp;&nbsp;Expert systems<\/h2>\n\n\n\n<dl>\n  <dt>  6.1\tOverview\t121 <\/dt>\n\n  <dt>  6.2\tWhat are expert systems?\t121 <\/dt>\n\n  <dt>  6.3\tUses of expert systems\t122 <\/dt>\n\n  <dt>  6.4\tArchitecture of an expert system\t123 <\/dt>\n  <dd><dl>\n      <dt>  6.4.1\tExplanation facility\t124 <\/dt>\n\n      <dt>  6.4.2\tDialogue component\t126 <\/dt>\n    <\/dl>\n\n  <dt>  6.5\tExamples of four expert systems\t127 <\/dt>\n  <dd><dl>\n      <dt>  6.5.1\tExample 1: MYCIN\t127 <\/dt>\n\n      <dt>  6.5.2\tExample 2: PROSPECTOR\t128 <\/dt>\n\n      <dt>  6.5.3\tExample 3: DENDRAL\t128 <\/dt>\n\n      <dt>  6.5.4\tExample 4: XCON\t129 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  6.6\tBuilding an expert system\t129 <\/dt>\n  <dd><dl>\n      <dt>  6.6.1\tKnowledge elicitation\t130 <\/dt>\n      <dd><dl>\n          <dt>  Techniques for knowledge elicitation\t130 <\/dt>\n\n          <dt>  Tool support for knowledge elicitation\t132 <\/dt>\n        <\/dl> <\/dd>\n\n      <dt>  6.6.2\tRepresenting the knowledge\t132 <\/dt>\n      <dd><dl>\n          <dt>  Expert system shells\t132 <\/dt>\n\n          <dt>  High-level programming languages\t133 <\/dt>\n\n          <dt>  Selecting a tool\t133 <\/dt>\n        <\/dl> <\/dd>\n    <\/dl>\n\n  <dt>  6.7\tLimitations of expert systems\t134 <\/dt>\n\n  <dt>  6.8\tHybrid expert systems\t135 <\/dt>\n\n  <dt>  6.9\tSummary\t135 <\/dt>\n\n  <dt>  6.10\tExercises\t135 <\/dt>\n\n  <dt>  6.11\tRecommended further reading\t136 <\/dt>\n<\/dl>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ch7\">7&nbsp;&nbsp;Natural language understanding<\/h2>\n\n\n\n<dl>\n  <dt>  7.1\tOverview\t137 <\/dt>\n\n  <dt>  7.2\tWhat is natural language understanding?\t137 <\/dt>\n\n  <dt>  7.3\tWhy do we need natural language understanding?\t138 <\/dt>\n\n  <dt>  7.4\tWhy is natural language understanding difficult?\t138 <\/dt>\n\n  <dt>  7.5\tAn early attempt at natural language understanding: SHRDLU\t140 <\/dt>\n\n  <dt>  7.6\tHow does natural language understanding work?\t141 <\/dt>\n\n  <dt>  7.7\tSyntactic analysis\t143 <\/dt>\n  <dd><dl>\n      <dt>  7.7.1\tGrammars\t144 <\/dt>\n\n      <dt>  7.7.2\tAn example: generating a grammar fragment\t145 <\/dt>\n\n      <dt>  7.7.3\tTransition networks\t147 <\/dt>\n\n      <dt>  7.7.4\tContext-sensitive grammars\t150 <\/dt>\n\n      <dt>  7.7.5\tFeature sets\t152 <\/dt>\n\n      <dt>  7.7.6\tAugmented transition networks\t153 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  7.8\tSemantic analysis\t153 <\/dt>\n  <dd><dl>\n      <dt>  7.8.1\tSemantic grammars\t154 <\/dt>\n      <dd><dl>\n          <dt>  An example: a database query interpreter revisited\t154 <\/dt>\n        <\/dl> <\/dd>\n\n      <dt>  7.8.2\tCase grammars\t155 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  7.9\tPragmatic analysis\t158 <\/dt>\n  <dd><dl>\n      <dt>  7.9.1\tSpeech acts\t159 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  7.10\tSummary\t160 <\/dt>\n\n  <dt>  7.11\tExercises\t160 <\/dt>\n\n  <dt>  7.12\tRecommended further reading\t161 <\/dt>\n\n  <dt>  7.13\tSolution to SHRDLU problem\t161 <\/dt>\n<\/dl>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ch8\">8&nbsp;&nbsp;Computer vision<\/h2>\n\n\n\n<p>The Prolog examples in this chapter use images coded in two formats a simple list of lists representation defined in <a href=\"prolog\/ch8\/image.p\">image.p<\/a>, and a more complex representation using Prolog meta-logical mechanisms defined in <a href=\"prolog\/ch8\/gimage.p\">gimage.p<\/a>.  The former is used for input and output, but the latter makes the definition of filters etc. easier.  The actual images used in the chapter are held in a file <a href=\"prolog\/ch8\/eximages.p\">eximages.p<\/a>.\n<\/p>\n\n\n\n<p>\nIf you are doing image processing, Prolog is not the best language choice!  However, we have included the Prolog examples to have a common language throughout.<\/p>\n\n\n\n<dl>\n  <dt>  8.1\tOverview\t163 <\/dt>\n\n  <dt>  8.2\tIntroduction\t163 <\/dt>\n  <dd><dl>\n      <dt>  8.2.1\tWhy computer vision is difficult\t163 <\/dt>\n\n      <dt>  8.2.2\tPhases of computer vision\t164 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  8.3\tDigitization and signal processing\t165 <\/dt>\n  <dd><dl>\n      <dt>  8.3.1\tDigitizing images\t165 <\/dt>\n\n      <dt>  8.3.2\tThresholding\t166 <\/dt>\n      <dd> <ul><li> <a href=\"prolog\/ch8\/threshold.p\">threshold.p<\/a> (Prolog) thresholding of grey-scale images <\/li><\/ul> <\/dd>\n\n      <dt>  8.3.3\tDigital filters\t169 <\/dt>\n      <dd> <ul><li> <a href=\"prolog\/ch8\/filter.p\">filter.p<\/a> (Prolog) linear filters including simple gradient filters <\/li><\/ul> <\/dd>\n      <dd><dl>\n          <dt>  Linear filters\t169 <\/dt>\n\n          <dt>  Smoothing\t170 <\/dt>\n\n          <dt>  Gaussian filters\t171 <\/dt>\n\n          <dt>  Practical considerations\t172 <\/dt>\n        <\/dl>\n    <\/dl> <\/dd>\n\n  <dt>  8.4\tEdge detection\t173 <\/dt>\n  <dd><dl>\n      <dt>  8.4.1\tIdentifying edge pixels\t173 <\/dt>\n      <dd><dl>\n          <dt>  Gradient operators\t174 <\/dt>\n\n          <dt>  Robert&#8217;s operator \t174 <\/dt>\n\n          <dt>  Sobel&#8217;s operator\t176 <\/dt>\n\n          <dt>  Laplacian operator\t178 <\/dt>\n\n          <dt>  Successive refinement and Marr&#8217;s primal sketch\t179 <\/dt>\n        <\/dl> <\/dd>\n\n      <dt>  8.4.2\tEdge following\t180 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  8.5\tRegion detection\t182 <\/dt>\n  <dd><dl>\n      <dt>  8.5.1\tRegion growing\t182 <\/dt>\n\n      <dt>  8.5.2\tThe problem of texture\t183 <\/dt>\n\n      <dt>  8.5.3\tRepresenting regions &#8212; quad-trees\t183 <\/dt>\n\n      <dt>  8.5.4\tComputational problems\t184 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  8.6\tReconstructing objects\t185 <\/dt>\n  <dd><dl>\n      <dt>  8.6.1\tInferring three-dimensional features\t185 <\/dt>\n      <dd><dl>\n          <dt>  Problems with labelling\t188 <\/dt>\n        <\/dl> <\/dd>\n\n      <dt>  8.6.2\tUsing properties of regions\t189 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  8.7\tIdentifying objects\t191 <\/dt>\n  <dd><dl>\n      <dt>  8.7.1\tUsing bitmaps\t191 <\/dt>\n\n      <dt>  8.7.2\tUsing summary statistics\t193 <\/dt>\n\n      <dt>  8.7.3\tUsing outlines\t194 <\/dt>\n\n      <dt>  8.7.4\tUsing paths\t195 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  8.8\tMultiple images\t197 <\/dt>\n  <dd><dl>\n      <dt>  8.8.1\tStereo vision\t197 <\/dt>\n\n      <dt>  8.8.2\tMoving pictures\t199 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  8.9\tSummary\t200 <\/dt>\n\n  <dt>  8.10\tExercises\t201 <\/dt>\n\n  <dt>  8.11\tRecommended further reading\t202 <\/dt>\n<\/dl>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ch9\">9&nbsp;&nbsp;Planning and robotics<\/h2>\n\n\n\n<p><img decoding=\"async\" align=\"left\" src=\"https:\/\/alandix.com\/books\/ai96\/images\/daleksm.jpg\" style=\"padding-right:0.5em\">The observant reader may have noticed    an uncanny resemblance between some of the robots depicted in this chapter and Daleks, the most famous adversaries of <a href=\"http:\/\/www.bbc.co.uk\/doctorwho\/\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\" (opens in a new tab)\">Dr Who<\/a>.    <\/p>\n\n\n\n<p>Read this <a href=\"http:\/\/news.bbc.co.uk\/1\/hi\/technology\/3764142.stm\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\" (opens in a new tab)\">BBC article<\/a> about a UN report in on the rise of domestic robots.<br>   <\/p>\n\n\n\n<dl>\n  <dt>  9.1\tOverview\t203 <\/dt>\n\n  <dt>  9.2\tIntroduction\t203 <\/dt>\n  <dd><dl>\n      <dt>  9.2.1\tFriend or foe?\t203 <\/dt>\n\n      <dt>  9.2.2\tDifferent kinds of robots\t204 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  9.3\tGlobal planning\t205 <\/dt>\n  <dd><dl>\n      <dt>  9.3.1\tPlanning actions &#8212; means-ends analysis\t205 <\/dt>\n      <dd> <ul><li> <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch9\/meansend.p\" target=\"_blank\" rel=\"noopener noreferrer\">meansend.p<\/a> (Prolog) means-end analysis <\/li><\/ul><\/dd>\n\n      <dt>  9.3.2\tPlanning routes &#8212; configuration spaces\t208 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  9.4\tLocal planning\t210 <\/dt>\n  <dd><dl>\n      <dt>  9.4.1\tLocal planning and obstacle avoidance\t210 <\/dt>\n\n      <dt>  9.4.2\tFinding out about the world\t213 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  9.5\tLimbs, legs and eyes\t216 <\/dt>\n  <dd><dl>\n      <dt>  9.5.1\tLimb control\t216 <\/dt>\n\n      <dt>  9.5.2\tWalking &#8212; on one, two or more legs\t220 <\/dt>\n\n      <dt>  9.5.3\tActive vision\t221 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  9.6\tPractical robotics\t223 <\/dt>\n  <dd><dl>\n      <dt>  9.6.1\tControlling the environment\t223 <\/dt>\n\n      <dt>  9.6.2\tSafety and hierarchical control\t224 <\/dt>\n    <\/dl> <\/dd>\n\n  <dt>  9.7\tSummary\t225 <\/dt>\n\n  <dt>  9.8\tExercises\t226 <\/dt>\n\n  <dt>  9.9\tRecommended further reading\t227 <\/dt>\n\n<\/dl>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ch10\">10&nbsp;&nbsp;Agents<\/h2>\n\n\n\n<dl> \n  <dt> 10.1 Overview 229  <\/dt>\n  <dt> 10.2 Software agents 229  <\/dt>\n  <dd>\n    <dl> \n      <dt> 10.2.1 The rise of the agent 230  <\/dt>\n      <dt> 10.2.2 Triggering actions 231  <\/dt>\n      <dt> 10.2.3 Watching and learning 232  <\/dt>\n      <dt> 10.2.4 Searching for information 234  <\/dt>\n    <\/dl> <\/dd>\n  <dt> 10.3 Co-operating agents and distributed AI 236  <\/dt>\n  <dd>\n    <dl> \n      <dt> 10.3.1 Blackboard architectures 236  <\/dt>\n      <dd> <ul><li> <a href=\"https:\/\/alandix.com\/code\/ai96\/prolog\/view\/ch10\/blackboard.p\" target=\"_blank\" rel=\"noopener noreferrer\">blackboard.p<\/a> \n        (Prolog) blackboard interpreter  <\/li><\/ul><\/dd>\n      <dt> 10.3.2 Distributed control 239  <\/dt>\n      <dt> 10.3.3 Emergent behaviour 240  <\/dt>\n      <dd> <ul><li> <a href=\"\/\/alandix.com\/code\/ai96\/prolog\/view\/ch10\/life.p\" target=\"_blank\" rel=\"noopener noreferrer\">life.p<\/a> \n        (Prolog) Conway&#8217;s Game of Life  <\/li>\n      <li> Eric Weisstein&#8217;s <a href=\"http:\/\/mathforum.org\/library\/view\/10854.html\" target=\"_blank\" rel=\"noopener noreferrer\">Treasure \n        Trove of Life<\/a> has lots of links related to Conway&#8217;s Game of Life <\/li><\/ul>  <\/dd>\n    <\/dl> <\/dd>\n  <dt> 10.4 Summary 242  <\/dt>\n  <dt> 10.5 Exercises 242  <\/dt>\n  <dt> 10.6 Recommended further reading 244  <\/dt>\n<\/dl>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ch11\">11&nbsp;&nbsp;Models of the mind<\/h2>\n\n\n\n<dl> \n  <dt> 11.1 Overview 245  <\/dt>\n  <dt> 11.2 Introduction 245  <\/dt>\n  <dt> 11.3 What is the human mind? 245  <\/dt>\n  <dt> 11.4 Production system models 247  <\/dt>\n  <dd>\n    <dl> \n      <dt> 11.4.1 ACT* 247  <\/dt>\n      <dt> 11.4.2 SOAR 249  <\/dt>\n    <\/dl> <\/dd>\n  <dt> 11.5 Connectionist models of cognition 251  <\/dt>\n  <dd> \n    <dl> \n      <dt> 11.5.1 Multi-layer perceptron 251  <\/dt>\n      <dd> <span class=\"warning-icon\"><\/span> page 255, <a href=\"https:\/\/alandix.com\/aibook\/first-edition\/errata\/#e4\">errata<\/a>  <\/dd>\n        17\/1\/2000<\/dd>\n      <dt> 11.5.2 Associative memories 256  <\/dt>\n      <dt> 11.5.3 Kohonen self-organizing networks 258  <\/dt>\n      <dd> <ul><li> <a href=\"kohonen.html\">kohonen<\/a> \n        a C package of programs implementing Kohonen nets  <\/li><\/ul><\/dd>\n    <\/dl> <\/dd>\n  <dt> 11.6 Summary 258  <\/dt>\n  <dt> 11.7 Exercises 258  <\/dt>\n  <dt> 11.8 Recommended further reading 259  <\/dt>\n  <dt> 11.9 Notes 260  <\/dt>\n<\/dl>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"ch12\">12&nbsp;&nbsp;Epilogue: philosophical and sociological issues<\/h2>\n\n\n\n<dl>\n  <dt>  12.1\tOverview\t261 <\/dt>\n\n  <dt>  12.2\tIntelligent machines or engineering tools?\t261 <\/dt>\n\n  <dt>  12.3\tWhat is intelligence?\t262 <\/dt>\n\n  <dt>  12.4\tComputational argument vs.Searle&#8217;s Chinese Room\t263 <\/dt>\n\n  <dt>  12.5\tWho is responsible? \t264 <\/dt>\n\n  <dt>  12.6\tMorals and emotions\t264 <\/dt>\n\n  <dt>  12.7\tSocial implications\t265 <\/dt>\n\n  <dt>  12.8\tSummary\t266 <\/dt>\n\n  <dt>  12.9\tRecommended further reading\t266 <\/dt>\n<\/dl>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"index\">Index<\/h2>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"biblio\">Bibliography<\/h2>\n","protected":false},"excerpt":{"rendered":"<p>Introduction What is artificial intelligence? 1 History of artificial intelligence 3 You can see more about SHRDLU on Terry Winnograd&#8216;s own HCI course pages And an online web version of Eliza you can talk to. The future for AI 7 1&nbsp;&nbsp;Knowledge in AI 1.1 Overview 9 1.2 Introduction 9 1.3 Representing knowledge 10 1.4 Metrics [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":2,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_themeisle_gutenberg_block_has_review":false,"footnotes":""},"class_list":["post-77","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/alandix.com\/aibook\/wp-json\/wp\/v2\/pages\/77","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/alandix.com\/aibook\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/alandix.com\/aibook\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/alandix.com\/aibook\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/alandix.com\/aibook\/wp-json\/wp\/v2\/comments?post=77"}],"version-history":[{"count":21,"href":"https:\/\/alandix.com\/aibook\/wp-json\/wp\/v2\/pages\/77\/revisions"}],"predecessor-version":[{"id":166,"href":"https:\/\/alandix.com\/aibook\/wp-json\/wp\/v2\/pages\/77\/revisions\/166"}],"up":[{"embeddable":true,"href":"https:\/\/alandix.com\/aibook\/wp-json\/wp\/v2\/pages\/2"}],"wp:attachment":[{"href":"https:\/\/alandix.com\/aibook\/wp-json\/wp\/v2\/media?parent=77"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}