5.1 – Hybrid System Literature Review

Game AI Pro – Bill Merrill’s Building Utility Decisions into Your Existing Behavior Tree Game AI Pro – Daniel Hilburn’s Simulating Behavior Trees: A Behavior Tree/Planner Hybrid Approach Game AI Pro – Sergio Ocio Barriales’s Building a Risk-Free Environment to…

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3.4 – GOAP Architecture

3.4.0 – The Design Different from all the previous architecture, GOAP, or Goal oriented action planner, is a planner–it is not only be responsive to the current scenario, but predicts future moves. To do that, it interpret the…

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3.3 – GOAP Demo 1

<GOAP name="GOAP_Test_1"> <VariableList> <!– Variable List –> <Variable id="EnemyInRange" key="0" let="200"></Variable> <!– true: let; false: gt–> <Variable id="EnemyLost" key="1"></Variable> <Variable id="EnemyDead" key="2"></Variable> </VariableList> <ActionList> <!– Action List –> <Action id="Search" cost="1"> <Precondition id="EnemyLost" value="true"></Precondition> <Effect id="EnemyLost" value="false"></Effect> </Action>…

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3.2 – GOAP C++ Libraries

Goal oriented action planner iteration 0 – use World State to represent the game world, use A* graph search for action planning, handle data loading from XML iteration 1 – build as a static library and integrate with UE4,…

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3.1 – Planner Literature Review

Peter Higley and Chris Conway’s GDC 2015 Talk on GOAP (0:00, 20:00) Jeff Orkin’s website on GOAP Jeff Orkin’s Applying Goal-Oriented Action Planning to Games (2003) Jeff Orkin’s Three States and a Plan: The AI of FEAR (GDC…

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