Goal-Oriented Action Planning (GOAP) är en AI-arkitektur som tillämpar ett måldrivet beteende åt agenter i spel. Mål uppnås genom att planer med åtgärder genereras med hjälp av en sökalgoritm. Syftet med denna rapport är att undersöka hur två sökalgoritmer, A* och IDA*, presterar under planering i GOAP. De experimenten som används är dels en miljö där agenter simuleras, samt ett test där planer genereras för samtliga implementerade mål utan rendering och simulering av agenter. Data som utvärderas är bl.a. planeringstiden, antal besökta noder under sökning och genererade planer. Utvärderingen visar en tydlig fördel till A*, som i snitt är 38 % snabbare än IDA* vid planering av åtgärder i GOAP. Slutsatsen blir att A* är den algoritm att föredra om prestanda är det som eftertraktas men IDA* kan motiveras för dess egenskaper, så som lägre minneskomplexitet. / <p>För tillgång till implementationen, maila f.helmesjo@gmail.com</p>

<p>An entity capable of assessing its and others action capabilities possess the power to predict how the involved entities may change their world. Through this knowledge and higher level of situation awareness, the assessing entity may choose the actions that have the most suitable effect, resulting in that entity’s desired world state.</p><p>This thesis covers aspects and concepts of an arbitrary planning system and presents a threat analyzer architecture built on the novel planning system Goal-Oriented Action Planning (GOAP). This planning system has been suggested for an application for improved missile route planning and targeting, as well as being applied in contemporary computer games such as F.E.A.R. – First Encounter Assault Recon and S.T.A.L.K.E.R.: Shadow of Chernobyl. The GOAP architecture realized in this project is utilized by two agents that perform action planning to reach their desired world states. One of the agents employs a modified GOAP planner used as a threat analyzer in order to determine what threat level the adversary agent constitutes. This project does also introduce a conceptual schema of a general planning system that considers orders, doctrine and style; as well as a schema depicting an agent system using a blackboard in conjunction with the OODA-loop.</p>

An architecture for actionbased planning and cooperation between multiple AI-agents based on the GOAP-architecture was developed together with a system to be used in advanced AI-courses at Linköping unversity. The architecture was implemented in this system to show the possibilities of our work.

The field of artificial intelligence has gained much knowledge through the implementation of decision-making systems in video games. One of these systems was the Goal Oriented Action Planning system (GOAP) which directs the behavior of an AI-agent through multiple digital artifacts categorized as goals, actions, and plans. The aim of the thesis is to aid in the understanding and creation of GOAP driven AI-agents in a video game setting to promote research on this topic. The research question of this thesis was about finding out how the GOAP architecture compares to other video game decision-making systems. The theoretical framework introduces the concept of the illusion of intelligence in video games and presents a discussion focused on the different components which make up a GOAP system and other components that support it. Additionally, the theoretical framework explains the need for a comparison between different decision-making systems and explains the social impact of game AI research. The methods section introduces the criteria for the comparison between GOAP and other decision-making systems and presents a comparison process that was driven by a literature review. A GOAP system was designed for this thesis using the unified modeling language and concept maps. It was then implemented using C# code in a free-of-charge game engine called Unity. We present the pseudocode for the implementation of the GOAP system and show that this framework is a modular, customizable, and reusable system that enables AI-agents to create plans from a varied set of actions. Finally, the paper suggests further research within game decision-making AI and emphasizes the importance of game AI research for communities of game developers, hobbyists, and others who could benefit from game AI in their projects.

An entity capable of assessing its and others action capabilities possess the power to predict how the involved entities may change their world. Through this knowledge and higher level of situation awareness, the assessing entity may choose the actions that have the most suitable effect, resulting in that entity’s desired world state. This thesis covers aspects and concepts of an arbitrary planning system and presents a threat analyzer architecture built on the novel planning system Goal-Oriented Action Planning (GOAP). This planning system has been suggested for an application for improved missile route planning and targeting, as well as being applied in contemporary computer games such as F.E.A.R. – First Encounter Assault Recon and S.T.A.L.K.E.R.: Shadow of Chernobyl. The GOAP architecture realized in this project is utilized by two agents that perform action planning to reach their desired world states. One of the agents employs a modified GOAP planner used as a threat analyzer in order to determine what threat level the adversary agent constitutes. This project does also introduce a conceptual schema of a general planning system that considers orders, doctrine and style; as well as a schema depicting an agent system using a blackboard in conjunction with the OODA-loop.