This flexibility enhances cognitive simulation studies by simplifying experimentation. Furthermore, SUE is very efficient, making it a candidate component for machine learning systems as well. We review the Structure-Mapping theory and describe the design of the engine. Next we demonstrate some examples of its operation. Finally, we discuss our plans for using SME in cognitive simulation studies.
Modeling crowd behavior is an important challenge for cognitive modelers. Models of crowd behavior facilitate analysis and prediction of the behavior of groups of people, who are in close geographical or logical states, and that are affected by each other's presence and actions. Existing models of crowd behavior, in a variety of fields, leave many open challenges. In particular, psychological models often offer only qualitative description, and do not easily permit algorithmic replication, while computer science models are often simplistic, treating agents as simple deterministic particles. We propose a novel model of crowd behavior, based on Festinger's Social Comparison Theory (SCT), a social psychology theory known and expanded since the early 1950's. We propose a concrete algorithmic framework for SCT, and evaluate its implementations in several crowd behavior scenarios. We show that our SCT model produces improved results compared to base models from the literature. We also discuss an implementation of SCT in the Soar cognitive architecture, and the question this implementation raises as to the role of social reasoning in cognitive architectures.
Historically, AI research has understandably focused on those aspects of cognition that distinguish humans from other animals - in particular, our capacity for complex problem solving. However, with a few notable exceptions, narratives in popular media generally focus on those aspects of human experience that we share with other social animals: attachment, mating and child rearing, violence, group affiliation, and inter-group and inter-individual conflict. Moreover, the stories we tell often focus on the ways in which these processes break down. In this paper, I will argue that current agent architectures don't offer particularly good models of these phenomena, and discuss specific phenomena that I think it would be illuminating to understand at a computational level.
Authoring narrative content for interactive digital media can be both difficult and time consuming.The research proposed here aims at enhancing the capabilities of content creators through the development of a computational model that improves the quality of automatically generated stories, potentially decreasing the burden placed on the author. The quality and believability of a story can be significantly enhanced by the presence of compelling characters. To achieve this objective, I aim to develop a choice-based computational model that facilitates the automatic generation of narrative that includes characters that are made more compelling by the presence of distinct personality characteristics.