Serious Games: Human-AI Interaction, Evolution, and Coevolution
Nandini Doreswamy (1, 2), Louise Horstmanshof (1) ((1) Southern Cross University, Lismore, New South Wales, Australia, (2) National Coalition of Independent Scholars)

TL;DR
This paper explores how Evolutionary Game Theory models can predict and analyze the dynamic coevolution of humans and AI in serious gaming contexts, highlighting potential equilibria and cooperation mechanisms.
Contribution
It examines three key EGT models—Hawk-Dove, Prisoner's Dilemma, and War of Attrition—in relation to human-AI interaction and coevolution, providing insights into strategic dynamics.
Findings
EGT models can predict balanced and asymmetric equilibria in human-AI interactions.
Repeated interactions and memory can foster cooperation between humans and AI.
Resource competition may lead to strategic coevolution and shared conventions.
Abstract
The serious games between humans and AI have only just begun. Evolutionary Game Theory (EGT) models the competitive and cooperative strategies of biological entities. EGT could help predict the potential evolutionary equilibrium of humans and AI. The objective of this work was to examine some of the EGT models relevant to human-AI interaction, evolution, and coevolution. Of thirteen EGT models considered, three were examined: the Hawk-Dove Game, Iterated Prisoner's Dilemma, and the War of Attrition. This selection was based on the widespread acceptance and clear relevance of these models to potential human-AI evolutionary dynamics and coevolutionary trajectories. The Hawk-Dove Game predicts balanced mixed-strategy equilibria based on the costs of conflict. It also shows the potential for balanced coevolution rather than dominance. Iterated Prisoner's Dilemma suggests that repeated…
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Taxonomy
TopicsArtificial Intelligence in Games · AI in Service Interactions · Reinforcement Learning in Robotics
MethodsEdge-augmented Graph Transformer
