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موضوع: ARTIFICIAL INTELLIGENCE FOR GAMES

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    عنوان کاربر
    تاریخ عضویت
    May 2011
    شماره عضویت
    30
    نوشته ها
    17
    تشکر
    0
    تشکر شده 5 بار در 4 ارسال

    ARTIFICIAL INTELLIGENCE FOR GAMES



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    ARTIFICIAL INTELLIGENCE FOR GAMES


    [code]1.1 The AI model 92.1 The AI model 362.2 AI Schematic 383.1 The AI model 413.2 The movement algorithm structure 433.3 The 2D movement axes and the 3D basis 453.4 The positions of characters in the level 453.5 The vector form of orientation 473.6 Smoothing facing direction of motion over multiple frames 493.7 A character using kinematic wander 563.8 Seek and flee 613.9 Seeking and arriving 633.10 Aligning over a 2π radians boundary 663.11 Seek moving in the wrong direction 713.12 Seek and pursue 723.13 The kinematic wander as a seek 773.14 The full wander behavior 783.15 Path following behavior 803.16 Predictive path following behavior 813.17 Vanilla and predictive path following 813.18 Path types 843.19 Coherence problems with path following 853.20 Separation cones for collision avoidance 893.21 Two in-cone characters who will not collide 903.22 Two out-of-cone characters who will collide 903.23 Collision avoidance using collision prediction 903.24 Collision ray avoiding a wall 943.25 Grazing a wall with a single ray, and avoiding it with three 973.26 Ray configurations for obstacle avoidance 973.27 The corner trap for multiple rays 983.28 Collision detection with projected volumes 98xxixxii List of Figures3.29 Steering family tree 993.30 Blending steering outputs 1013.31 The three components of flocking behaviors 1033.32 The neighborhood of a boid 1043.33 An unstable equilibrium 1053.34 A stable equilibrium 1053.35 Can’t avoid an obstacle and chase 1063.36 Missing a narrow doorway 1063.37 Long distance failure in a steering behavior 1073.38 Priority steering avoiding unstable equilibrium 1113.39 An imminent collision during pursuit 1123.40 A context-sensitive wall avoidance 1133.41 Steering pipeline 1143.42 Collision avoidance constraint 1163.43 Taking a run up to achieve a target velocity 1173.44 Obstacle avoidance projected and at right angles 1253.45 Parabolic arc 1273.46 Two possible firing solutions 1313.47 Projectile moving with drag 1333.48 Refining the guess 1363.49 Jump points between walkways 1423.50 Flexibility in the jump velocity 1423.51 A jump to a narrower platform 1433.52 Three cases of difficult jump points 1443.53 A one-direction chasm jump 1503.54 A selection of formations 1523.55 A defensive circle formation with different numbers of characters 1533.56 Emergent arrowhead formation 1543.57 Two-level formation motion in a V 1553.58 Nesting formations to greater depth 1663.59 Nesting formations shown individually 1663.60 An RPG formation, and two examples of the formation filled 1683.61 Different total slot costs for a party 1693.62 A baseball double play 1753.63 A corner kick in soccer 1763.64 Bounding overwatch 1773.65 Formation patterns match cover points 1783.66 An example of slot change in bounding overwatch 1793.67 Requested and filtered accelerations 1813.68 A J-turn emerges 1823.69 Everything is filtered: nothing to do 1823.70 Heuristics make the right choice 1843.71 Decision arcs for motor vehicles 186xxiii3.72 Infinite number of orientations per vector 1913.73 Local rotation axes of an aircraft 1984.1 The AI model 2044.2 A general graph 2054.3 A weighted graph 2064.4 Total path cost 2074.5 Weighted graph overlaid onto level geometry 2084.6 A directed weighted graph 2094.7 All optimal paths 2124.8 Dijkstra at the first node 2134.9 Dijkstra with a couple of nodes 2134.10 Open node update 2154.11 Following the connections to get a plan 2164.12 Dijkstra in steps 2224.13 A* estimated-total-costs 2244.14 Closed node update 2264.15 Priority heap 2334.16 Bucketed priority queues 2344.17 Euclidean distance heuristic 2414.18 Euclidean distance fill characteristics 2424.19 The cluster heuristic 2434.20 Fill patterns indoors 2444.21 Fill patterns outdoors 2454.22 Two poor quantizations show that a path may not be viable 2484.23 Tile-based graph with partially blocked validity 2504.24 Tile-based plan is blocky 2504.25 Dirichlet domains as cones 2514.26 Problem domains with variable falloff 2524.27 Path with inflections at vertices 2544.28 Points of visibility graph bloat 2554.29 Polygonal mesh graph 2564.30 Quantization into a gap 2574.31 Non-interpolation of the navigation mesh 2584.32 Portal representation of a navigation mesh 2594.33 Different node positions for different directions 2594.34 A non-translational tile-based world 2604.35 Plan on a non-translational tile graph 2614.36 Smoothed path with a better smoothing indicated 2624.37 Hierarchical nodes 2664.38 A hierarchical graph 2674.39 A tile-based representation of a level with groups marked 2674.40 Switching off nodes as the hierarchy is descended 2734.41 Pathological example of the minimum method 274xxiv List of Figures4.42 Pathological example of the maximin method 2744.43 The delegation in progress 2764.44 An instance in the world graph 2774.45 Police car moving along a four-lane road 2874.46 Different nodes with different times and the same position 2884.47 Navigating a gap through oncoming traffic 2894.48 When top speed isn’t a good idea 2904.49 The placement of nodes within the same lane 2914.50 Velocity diagram for allowed animations 2944.51 Position diagram for allowed animations 2954.52 Example of an animation state machine 2974.53 Example of position ranges for animations 2984.54 The dangerous room 2984.55 The example path through the dangerous room 2995.1 The AI model 3025.2 Decision making schematic 3035.3 A decision tree 3045.4 The decision tree with a decision made 3055.5 Trees representing AND and OR 3065.6 Wide decision tree with decision 3075.7 Deep binary decision tree 3085.8 Flat decision tree with four branches 3085.9 Balanced and unbalanced trees 3135.10 Merging branches 3145.11 Pathological tree 3145.12 Random tree 3155.13 A simple state machine 3195.14 The basic cleaning robot state machine 3285.15 An alarm mechanism in a standard state machine 3295.16 A hierarchical state machine for the robot 3295.17 A hierarchical state machine with a cross hierarchy transition 3305.18 Current state in a hierarchy 3315.19 Hierarchical state machine example 3335.20 State machine with decision tree transitions 3415.21 State machine without decision tree transitions 3425.22 Membership functions 3465.23 Membership function for enumerated value 3475.24 Impossible defuzzification 3485.25 Minimum, average bisector, and maximum of the maximum 3495.26 Membership function cropped, and all membership functions cropped 3505.27 Enumerated defuzzification in a range 3525.28 Exclusive mapping to states for fuzzy decision making 3565.29 Why to replace transposition entries lower down 396xxv5.30 Schematic of a rule-based system 4045.31 UML of the matching system 4175.32 A Rete 4245.33 A join node with variable clash, and two others without 4265.34 The Rete with data 4285.35 Rete in mid-update 4315.36 Rete after-update 4325.37 Schematic of the rule sets in the game 4345.38 Blackboard architecture 4385.39 A parse tree 4555.40 State machine with transition states 4615.41 The action manager in context 4716.1 The AI model 4746.2 Tactical points are not the best pathfinding graph 4766.3 Ambush points derived from other locations 4776.4 Topological analysis of a waypoint graph 4786.5 A character selecting a cover point in two different ways 4826.6 Tactical information in a decision tree 4856.7 Distance problems with cover selection 4876.8 Good cover and visibility 4926.9 Order dependence in condensation checks 4966.10 An example influence map 5036.11 The security level of the influence map 5066.12 Influence map problems with lack of knowledge 5076.13 Learning a frag-map 5116.14 Tactical analyses of differing complexity 5126.15 The combined analyses 5156.16 Screenshot of a Gaussian blur on an influence map 5286.17 Screenshot of a sharpening filter on an influence map 5326.18 A cellular automaton 5336.19 Updating a cellular automaton 5356.20 Averaging the connection cost sometimes causes problems 5396.21 Screenshot of the planning system showing tactical pathfinding 5416.22 Adding waypoints that are not tactically sensible 5436.23 An example of multi-tier AI 5456.24 Multi-tiered AI and the player don’t mix well 5486.25 A multi-tier AI involving the player 5496.26 A hierarchical scheduling system for multi-tier AI 5516.27 State machines for emergent fire team behavior 5526.28 An action sequence needing timing data 5546.29 Taking a room 5606.30 Taking various rooms 561xxvi List of Figures7.1 The energy landscape of a one-dimensional problem 5687.2 Hill climbing ascends a fitness landscape 5707.3 Non-monotonic fitness landscape with sub-optimal hill climbing 5737.4 Random fitness landscape 5737.5 Non-monotonic fitness landscape solved by momentum hill climbing 5747.6 Hill climbing multiple trials 5757.7 Different window sizes 5877.8 Different windows in a five choice game 5877.9 A decision tree 5947.10 The decision tree constructed from a simple example 5977.11 Two sequential decisions on the same attribute 6077.12 The example tree in ID4 format 6087.13 Decision tree before ID4 6107.14 Decision tree mid-ID4 6117.15 Decision tree after ID4 6117.16 A learned state machine 6237.17 A learned machine with additional rewards 6237.18 ANN architectures (MLP and Hopfield) 6307.19 Perceptron algorithm 6317.20 Multi-layer perceptron architecture 6347.21 The sigmoid threshold function 6357.22 Bias and the sigmoid basis function 6427.23 The radial basis function 6427.24 Grid architecture for Hebbian learning 6447.25 Influence mapping with Hebbian learning 6458.1 Tic-Tac-Toe game tree 6508.2 Abstract game tree showing terminal and players’ moves 6518.3 The game tree of 7-Split-Nim 6528.4 A one-ply decision making process 6548.5 One-ply tree, my move 6558.6 One-ply tree, opponent’s move 6558.7 The two-ply game tree 6568.8 Negamax values bubbled up a tree 6598.9 An optimizable branch 6628.10 AB negamax calls on a game tree 6638.11 The game tree with negascout calls 6698.12 A game tree showing strategies 6909.1 AI slicing 6959.2 Behaviors in phase 6969.3 Relatively prime 6969.4 Wright’s phasing algorithm 7019.5 Behaviors in a hierarchical scheduling system 707xxvii9.6 Hierarchical LOD algorithms 7229.7 The behaviors being run in the hierarchical LOD 7239.8 Distribution-based group LOD 7239.9 Normal distribution curve 7249.10 Power law distribution 72410.1 A set of sight cones 74510.2 Attenuation in action 75110.3 Angled corridor and a sound transmission error 75710.4 Transmission through walls 75710.5 Timing discrepancy for moving characters 75810.6 Sense graph for one-way glass 76010.7 Connection positions in a sense graph 76110.8 Air conditioning in a sense graph 76110.9 Line of sight in a sight-connected pair of nodes 76210.10 The sight sense graph 76311.1 Dirichlet domains misclassifying a corridor 77211.2 A visibility-based graph and its post-processed form 77511.3 A crevice from automatic geometry widening 78111.4 AI geometries: rendering, physics, and AI 78211.5 The SimBionic editor screen 78612.1 The AI model 79212.2 AI architecture for a shooter 79912.3 AI architecture for race driving 80612.4 AI architecture for urban driving 80612.5 AI architecture for RTS games 80912.6 AI architecture for sports games 81312.7 AI architecture for turn-based strategy games 81513.1 Neural network architecture for creature-teaching games 82013.2 A finite state machine for a simple creature 82413.3 The simple behavior of a single predator 826[/code]


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  2. 2 کاربر به خاطر ارسال مفید javad از ایشان تشکر کرده اند:

    amir js (01-26-2014),unitypoker (10-04-2016)

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  1. Creating 2D Games in Unity
    توسط sajad98 در انجمن یونیتی
    پاسخ: 4
    آخرين نوشته: 06-09-2013, 01:17 PM

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