I denna uppsats kommer vi att presentera en modell vars syfte är att generera beteende-mönster för rollfigurer i digitala spel. Spelgenren rollspel (eng. Role-playing games) placerarspelaren i en värld fylld av fantastiska monster och modiga hjältar. I ett sådant spel ärde goda karaktärerna minst lika viktiga som de ondskefulla varelser som spelaren kämparmot. Hur ser livet ut för en spelkaraktär när den inte hjälper spelaren på dess äventyr? Dekanske lever som fiskare på havet eller som bönder ute på fältet. Mer troligt är att de bori en by tillsammans med en massa andra spännande karaktärer. Vi undersöker hur sådanakaraktärers vardag ser ut och tittar på en teknik som används för att skapa deras beteen-demönster, Cyclic Scheduling. Tekniken innebär att utvecklare skapar scheman som styrrollfigurernas beteende. Dessa scheman måste skapas i förväg under spelets utveckling ochkräver i stora spel många arbetstimmar för att utvecklas. Modellen vi presenterar i dennauppsats använder tekniken Smart Terrain för att automatiskt generera beteendemönsteroch kan användas för att minska utvecklingstid. Vi diskuterar hur modellen kan användas iföränderliga spelvärldar där utvecklare inte i förhand vet hur spelens omgivningar kommeratt se ut. / In this thesis we present a model for the generation of behaviour patterns for charactersin digital games. In the genre Role-playing games the player is placed in a world filledwith fantastic monsters and brave heroes. In this kind of game the good characters areas important as the evil creatures the player must fight against. What kind of life doesa game character have when not helping the player on adventures? Maybe they live asfishermen on the sea or as farmers in the fields. More likely they live in villages amongstother exciting game characters. We examine what these characters’ daily routines looks likeand look at a technique used for creating their behaviour patterns, Cyclic Scheduling. Thetechnique is used by developers to create schedules that control the behaviour of charactersin games. These schedules have to be created during the game development process andfor bigger games this consumes a lot of time. The model we presents in this thesis uses thetechnique Smart Terrain to automatically generate behaviour patterns thereby reducingthe development time. We discuss how the model can be used in dynamic game worldswhere the developers are unaware of potential changes in the game world.

Ant colony optimization (ACO) is an algorithm which simulates ant foraging behavior. When ants search for food they leave pheromone trails to tell other ants which paths to take to find food. ACO has been adapted to many different problems in computer science: mainly variations on shortest path algorithms for graphs and networks. ACO can be adapted to work as a form of communication between separate agents in a video game AI. By controlling the effectiveness of this communication, the difficulty of the game should be able to be controlled. Experimentation has shown that ACO works effectively as a form of communication between agents and supports that ACO is an effective form of difficulty control. However, further experimentation is needed to definitively show that ACO is effective at controlling difficulty and to show that it will also work in a large scale system.

In the field of artificial intelligence the production of believable emotions are vital to be able to produce believable behavior of virtual agents. This is done with a process called affective appraisal, which means that events and situations are appraised and emotions are produced accordingly. The Artificial Intelligence and Computer Graphics (AICG) lab at Linköpings University has been devel- oping an AI architecture for virtual agents. This architecture had an appraisal module in need of improvement. This M.Sc. thesis had the purpose of doing this. Several approaches to affective appraisal are discussed and compared and finally one approach, called the OCC model, is chosen for implementation. This model is suitable for a real-time AI architecture as it is simple, easy to implement and can produce a wide range of emotions. The implementation of the OCC model is described in terms of how its different parts are incorporated into the previously existing AI architecture. Three extensions to the OCC model are also implemented to improve the results: emotional memories, the appraisal of unexpected events and interaction between the produced emotions. Finally the implementation is tested and the results of the tests are discussed. It is found that the implementation produces sufficient results for the scope of the thesis and for the requirements of the AI architecture into which it is incorporated.

Abstract. Autonomous agents, also known as AI agents, are staples in modern video games. They take a lot of roles, everything from being quest-givers in roleplaying games, to opposing forces in action- and shooter games. Crafting an AI that is not only easy to create, but also retains humanlike and believable behavior, has always represented a challenge to the development industry, and has in several cases ended up with open world games using AI systems that limit the AI agents to simple moving patterns. In this thesis, a form of AI systems more commonly used in simulation games such as The Sims video game series, are taken and implemented in an environment that could possibly be seen in an open world game. After the implementation, a set of tests were performed on a group of testers which resulted in the insight that a majority of the testers, when asked to compare their experience to other games, found this implementation to feel more lifelike and realistic.