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ALGORITHMS FOR STEERING SOFTBOTS IN GAME WORLDS Pros Can be searched using A heuristic search Commonly used paths can be stored for quick access Cons Worst-case complexity is exponential in nature Paths may look unrealistic and require post-processing in some cases 1.GRID-BASED METHODS 2.NAVIGATION MESHES 3.REACTIVE METHODS 4.AGENT-BASED APPROACHES 5.HYBRID APPROACHES 6.HARDCODED SYSTEMS 9/24/09VITALE1

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ALGORITHMS FOR STEERING SOFTBOTS IN GAME WORLDS Pros Partitions the environments terrain into polygons Waypoints are used to connect points that create paths Cons Works best with static environments Similar issues to Grid-Based Methods 1.GRID-BASED METHODS 2.NAVIGATION MESHES 3.REACTIVE METHODS 4.AGENT-BASED APPROACHES 5.HYBRID APPROACHES 6.HARDCODED SYSTEMS 9/24/09VITALE2

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ALGORITHMS FOR STEERING SOFTBOTS IN GAME WORLDS Pros Apply local environmental information to generate movement Characters are react from intrinsic information within the landscape Reactions are determined on a per-application basis, most often using potential fields Cons Bots often get stuck from poorly designed potential fields 1.GRID-BASED METHODS 2.NAVIGATION MESHES 3.REACTIVE METHODS 4.AGENT-BASED APPROACHES 5.HYBRID APPROACHES 6.HARDCODED SYSTEMS 9/24/09VITALE3

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ALGORITHMS FOR STEERING SOFTBOTS IN GAME WORLDS Pros Used often in computer graphics Behavior is determined by: a specified set of rules social forces particle swarm methods Cons Requires quantifying, identifying and controlling abstract knowledge and information 1.GRID-BASED METHODS 2.NAVIGATION MESHES 3.REACTIVE METHODS 4.AGENT-BASED APPROACHES 5.HYBRID APPROACHES 6.HARDCODED SYSTEMS 9/24/09VITALE4

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ALGORITHMS FOR STEERING SOFTBOTS IN GAME WORLDS Pros Combines several approaches in one Can consider local and global information Cons Difficult to design and implement Requires in-depth knowledge of the problem 1.GRID-BASED METHODS 2.NAVIGATION MESHES 3.REACTIVE METHODS 4.AGENT-BASED APPROACHES 5.HYBRID APPROACHES 6.HARDCODED SYSTEMS 9/24/09VITALE5

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ALGORITHMS FOR STEERING SOFTBOTS IN GAME WORLDS Pros Always engineered with the optimal path solution Direct-control of agent steering Cons Very restrictive Unresponsive to the smallest change Non-autonomous 1.GRID-BASED METHODS 2.NAVIGATION MESHES 3.REACTIVE METHODS 4.AGENT-BASED APPROACHES 5.HYBRID APPROACHES 6.HARDCODED SYSTEMS 9/24/09VITALE6

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PATH PLANNING IN GAMES WITH CA Reynolds & Kinnaird-Heether [2008] WCCI 2008 Competition; Socially motivated, agent-based approach: Cultural Algorithms 3D racing environment Parameterizes rules for an single racecar driver RESULTS: Steers a car around a track 9/24/09VITALE7

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EXAMPLE OF CA OPTIMIZATION Reynolds & Kinnaird-Heether Applied CA to learn racing parameters Came in second at the WCCI 2008 How can this approach be be scaled up? 9/24/09VITALE8 acceleration(speedX, maxSpeed) if(speedX