by David A. Casta? / Prepared under ONR Contract no. N00014-76-C-0346. Originally presented as the author's thesis, (Ph.D.), M.I.T. Dept. of Mathematics, 1976. / Bibliography: p.142-147.

In evolutionary theory the existence of self-sacrificing cooperative traits poses a problem that has engendered decades of debate. The principal theories of the evolution of altruism are inclusive fitness, reciprocal altruism, and multilevel selection. To provide a framework for the unification o f these apparently disparate theories, this dissertation identifies two fundamental conditions required for the evolution of altruism: 1) non-zero-sum fitness benefits for cooperation and 2) positive assortment among altruistic behaviors. I demonstrate the underlying similarities in these three theories in the following two ways. First, I show that the game-theoretic model of the prisoner’s dilemm a (PD) is inherent to all three theories. While the PD has been used extensively to model reciprocal altruism, I demonstrate that the n-player PD captures fundamental aspects o f multilevel selection and inclusive fitness in that NPD model parameters relate simply to Simpson’s paradox, the Price covariance equation, and Hamilton’s rule. The tension between hierarchical levels that defines a PD reflects the tension between Abstract levels o f selection that is explicit in multilevel selection theory, and im plicit in the other two theories. Second, Ham ilton’s rule from inclusive fitness theory applies to the other theories. As mentioned, I demonstrate that this rule relates to multilevel selection via the NPD. I also show that Queller’s generalization of Hamilton’s rule applies to the conditional strategies of reciprocal altmism. This challenges the selfish-gene viewpoint by highlighting the fact that it is the phenotypes o f others, not their genotypes, that is critical to the evolution o f altruism. I integrate the PD and H am ilton’s rule as follows: the evolution o f altruism in general involves PD situations in which Hamilton’s rule specifies the necessary relationship between 1) the degree of non-zero-sumness within the PD and 2) the degree of positive assortment among altruistic behaviors. Additional contributions of this research include a demonstration that randomly formed associations can provide the necessary positive assortment for strong altruism to evolve, the development of a new selection decomposition that is symmetrical to the Price equation, and a game-theoretic analysis showing the essential similarity of weak and strong altruism under selection.

Doctorat en sciences psychologiques / info:eu-repo/semantics/nonPublished

Cette thèse aborde la problématique de la création d’intelligences artificielles (IA) contrôlant la prise de décision haut-niveau dans les jeux de stratégie. Ce type de jeux propose des environnements complexes nécessitant de manipuler de nombreuses ressources en faisant des choix d’actions dépendant d’objectifs à long terme. La conception de ces IA n’est pas simple car il s’agit de fournir une expérience pour le joueur qui soit divertissante et intéressante à jouer. Ainsi, le but n’est pas d’obtenir des comportements d’IA imbattables, mais plutôt de refléter différents traits de personnalités permettant au joueur d’être confronté à des adversaires diversifiés. Leur conception fait intervenir des game designers qui vont définir les différentes stratégies en fonction de l’expérience qu’ils souhaitent créer pour le joueur, et des développeurs qui programment et intègrent ces stratégies au jeu. La collaboration entre eux nécessite de nombreux échanges et itérations de développement pour obtenir un résultat qui correspond aux attentes des designers. L’objectif de cette thèse est de proposer une solution de modélisation de stratégies accessible aux game designers en vue d’améliorer et de simplifier la création de comportements stratégiques. Notre proposition prend la forme d’un moteur stratégique choisissant des objectifs à long terme et vient se placer au dessus d’un module tactique qui gère l’application concrète de ces objectifs. La solution proposée n’impose pas de méthode pour résoudre ces objectifs et laisse libre le fonctionnement du module tactique. Le moteur est couplé à un modèle de stratégie permettant à l’utilisateur d’exprimer des règles permettant au moteur de choisir les objectifs et de leur allouer des ressources. Ces règles permettent d’exprimer le choix d’objectifs en fonction du contexte, mais également d’en choisir plusieurs en parallèle et de leur donner des importances relatives afin d’influencer la répartition des ressources. Pour améliorer l’intelligibilité nous utilisons un modèle graphique inspiré des machines à états finis et des behavior trees. Les stratégies créées à l’aide de notre modèle sont ensuite exécutées par le moteur de stratégie pour produire des directives qui sont données au module tactique. Ces directives se présentent sous la forme d’objectifs stratégiques et de ressources qui leur sont allouées en fonction de leurs besoins et de l’importance relative qui leur a été donnée. Le module stratégique permet donc de rendre accessible la conception du niveau stratégique d’une IA contrôlant un adversaire dans un jeu de stratégie. / This PhD thesis addresses the topic of creating artificial intelligence (AI) to control high-level decision-making in strategy games. This kind of game offers complex environments that require the manipulation of a large number of resources by choosing actions depending on long-term goals. This AI design is not simple because it is about providing to the player a playful and interesting experience. Hence, the aim is not to create unbeatable behaviors, but rather to display several personality traits allowing the player to face diverse opponents. Its creation involves game designers who are responsible of defining several strategies according to the experience they want to provide to the player, and game developers who implement those strategies to put them into the game. The collaboration between them requires many exchanges and development iterations to obtain a result corresponding to game designers’ expectations. The objective of this PhD thesis is to improve and simplify the creation of strategical behaviors by proposing a strategy model intelligible to game designers and that can be interfaced easily with developers’ work. For game designers, a strategy model has been created to allow them to express rules guiding the choice of goals and their allocated resources. These rules make it possible for game designers to express which goal to choose according to the context but also to choose several of them and give them relative importance in order to influence the resource distribution. To improve intelligibility we use a graphical model inspired from finite state machines and behavior trees. Our proposition also includes a strategy engine which executes the strategies created with the model. This execution produces directives that are represented by a list of selected strategical goals and the resources that have been allocated according to the importance and needs of each goal. These directives are intended for a tactical module in charge of their application. The developers are then responsible for the implementation of this tactical module. Our solution enables game designers to directly design the strategical level of an AI and therefore facilitates their cooperation with game developers and simplifies the entire creation process of the AI.