_index.md (7834B)
1 +++ 2 title = "Intelligent Systems" 3 +++ 4 # Intelligent Systems 5 6 core topics: 7 * search & heuristics 8 * knowledge 9 * adaptivity 10 11  12 13 Table of contents: 14 * [Assessment information](assessment-info) 15 * [State Space Representations Intro](state-space-repr-intro) 16 * [State space search](state-space-search) 17 * [Uninformed search strategies](state-space-search#uninformed-search-strategies) 18 * [Breadth-first (BF) search](state-space-search#breadth-first-bf-search) 19 * [Depth-first (DF) search](state-space-search#depth-first-df-search) 20 * [Depth-limited search](state-space-search#depth-limited-search) 21 * [Iterative deepening search](state-space-search#iterative-deepening-search) 22 * [Informed search (heuristics)](state-space-search#informed-search-heuristics) 23 * [A Search](state-space-search#a-search) 24 * [A* Search](state-space-search#a-search-1) 25 * [Adversarial search](state-space-search#adversarial-seach) 26 * [Minimax](state-space-search#minimax) 27 * [Setup](state-space-search#setup) 28 * [Optimal strategies](setup#optimal-strategies) 29 * [Evaluation](state-space-search#evaluation) 30 * [Reducing problems of complexity with Minimax](state-space-search#reducing-problems-of-complexity-with-minimax) 31 * [Cutting off search:](state-space-search#cutting-off-search) 32 * [Alpha-Beta pruning (efficient Minimax)](state-space-search#alpha-beta-pruning-efficient-minimax) 33 * [Search with no or partial information](state-space-search#search-with-no-or-partial-information) 34 * [Perfect information Monte Carlo sampling (rdeep)](state-space-search#perfect-information-monte-carlo-sampling-rdeep) 35 * [Games with chance](state-space-search#games-with-chance) 36 * [Summary (Schnapsen)](state-space-search#summary-schnapsen) 37 * [Search direction](state-space-search#search-direction) 38 * [Rational agents](rational-agents) 39 * [Agents](rational-agents#agents) 40 * [Rationality](rational-agents#rationality) 41 * [Task environments](rational-agents#task-environments) 42 * [Agent types](rational-agents#agent-types) 43 * [Simple Reflex](rational-agents#simple-reflex) 44 * [Reflex & State](rational-agents#reflex-state) 45 * [Goal-Based](rational-agents#goal-based) 46 * [Learning](rational-agents#learning) 47 * [Logical agents](logical-agents) 48 * [What is logic](logical-agents#what-is-logic) 49 * [Syntax](logical-agents#syntax) 50 * [Propositional logic (PL)](logical-agents#propositional-logic-pl) 51 * [First order logic (FOL)](logical-agents#first-order-logic-fol) 52 * [Basic elements:](logical-agents#basic-elements) 53 * [Sentences](logical-agents#sentences) 54 * [Quantification](logical-agents#quantification) 55 * [Universal quantification](logical-agents#universal-quantification) 56 * [Existential quantification](logical-agents#existential-quantification) 57 * [Quantifier Duality](logical-agents#quantifier-duality) 58 * [Decidability vs undecidability](logical-agents#decidability-vs-undecidability) 59 * [Knowledge engineering in FOL](logical-agents#knowledge-engineering-in-fol) 60 * [Choice of formalisms](logical-agents#choice-of-formalisms) 61 * [Propositionalising FOL](logical-agents#propositionalising-fol) 62 * [Reduction to propositional inference](logical-agents#reduction-to-propositional-inference) 63 * [Universal instantiation (UI):](logical-agents#universal-instantiation-ui) 64 * [Existential instantiation (EI):](logical-agents#existential-instantiation-ei) 65 * [Applying in Schnapsen - Strategies (examples)](logical-agents#applying-in-schnapsen-strategies-examples) 66 * [Play Jack](logical-agents#play-jack) 67 * [Play cheap](logical-agents#play-cheap) 68 * [Play trump marriage](logical-agents#play-trump-marriage) 69 * [Semantics](logical-agents#semantics) 70 * [Interpretations & Models](logical-agents#interpretations-models) 71 * [Entailment](logical-agents#entailment) 72 * [Truth](logical-agents#truth) 73 * [Validity](logical-agents#validity) 74 * [Satisfiability](logical-agents#satisfiability) 75 * [Calculus (algorithms for inference)](logical-agents#calculus-algorithms-for-inference) 76 * [Properties of inference](logical-agents#properties-of-inference) 77 * [Proof methods](logical-agents#proof-methods) 78 * [Model checking & search](logical-agents#model-checking-search) 79 * [Truth Tables for inference](logical-agents#truth-tables-for-inference) 80 * [Effective proofs by model checking](logical-agents#effective-proofs-by-model-checking) 81 * [Clause Normal Form (CNF)](logical-agents#clause-normal-form-cnf) 82 * [DPLL algorithm](logical-agents#dpll-algorithm) 83 * [Heuristic search in DPLL](logical-agents#heuristic-search-in-dpll) 84 * [Satisfiability modulo theory](logical-agents#satisfiability-modulo-theory) 85 * [Rule-based reasoning](logical-agents#rule-based-reasoning) 86 * [Inference rules](logical-agents#inference-rules) 87 * [Searching for proofs](logical-agents#searching-for-proofs) 88 * [Forward and backward chaining](logical-agents#forward-and-backward-chaining) 89 * [Resolution](logical-agents#resolution) 90 * [Probability and Uncertainty](probability-uncertainty) 91 * [Vagueness: Fuzzy Set Theory](probability-uncertainty#vagueness-fuzzy-set-theory) 92 * [Fuzzy sets](probability-uncertainty#fuzzy-sets) 93 * [Fuzzy relations](probability-uncertainty#fuzzy-relations) 94 * [Evaluation](probability-uncertainty#evaluation) 95 * [Uncertainties: Probability Theory](probability-uncertainty#uncertainties-probability-theory) 96 * [General](probability-uncertainty#general) 97 * [Axioms of probability](probability-uncertainty#axioms-of-probability) 98 * [Joint probability distributions](probability-uncertainty#joint-probability-distributions) 99 * [Bayesian networks](probability-uncertainty#bayesian-networks) 100 * [Evaluation of probabilities](probability-uncertainty#evaluation-of-probabilities) 101 - [Machine Learning](machine-learning) 102 - [Learning problems](machine-learning#learning-problems) 103 - [Methodology](machine-learning#methodology) 104 - [Data](machine-learning#data) 105 - [Experimentation](machine-learning#experimentation) 106 - [Evaluation](machine-learning#evaluation) 107 - [Machine Learning Steps:](machine-learning#machine-learning-steps) 108 - [Choose the features](machine-learning#choose-the-features) 109 - [Inductive learning method](machine-learning#inductive-learning-method) 110 - [Classifying with naive Bayes](machine-learning#classifying-with-naive-bayes) 111 - [Clustering with K-nearest neighbor](machine-learning#clustering-with-k-nearest-neighbor) 112 - [Linear classifier](machine-learning#linear-classifier) 113 - [Support vector machine](machine-learning#support-vector-machine) 114 - [Choose the model (model search)](machine-learning#choose-the-model-model-search) 115 - [Regression](machine-learning#regression) 116 - [Gradient descent](machine-learning#gradient-descent) 117 - [Neural Networks](machine-learning#neural-networks) 118 - [Overview](machine-learning#overview) 119 - [Training neural networks](machine-learning#training-neural-networks) 120 - [Autoencoders: a NN architecture](machine-learning#autoencoders-a-nn-architecture) 121 - [Trying it out](machine-learning#trying-it-out) 122 - [The promise of depth](machine-learning#the-promise-of-depth) 123 * [Ethics of AI](ethics) 124 * [Sci-fi ethics (problems down the road)](ethics#sci-fi-ethics-problems-down-the-road) 125 * [Today's problems](ethics#today-s-problems) 126 * [Philosophy of AI](philosophy)