This paper describes our efforts to model perceptual attention in virtual humans for the joint synthetic battlefield. With the exception of the work by Reece et al. [19,20] to develop individual combatants, current computer generated forces represent entities at the platform level, hence, the perceptual model of these entities is typically a composite representing the behaviors of multiple huma...

We present an instance-based, online method for learning action models in unanticipated, relational domains. Our algorithm memorizes preand post-states of transitions an agent encounters while experiencing the environment, and makes predictions by using analogy to map the recorded transitions to novel situations. Our algorithm is implemented in the Soar cognitive architecture, integrating its t...

This paper explores the role of spontaneous retrieval in prospective memory, in an agent implemented in the Soar cognitive architecture. At goal initiation time, spreading activation causes the goal to be the most activated element in long-term memory, at which point it is spontaneously retrieved into working memory and pursued. We show that goal encoding specificity increases prospective memor...

Psychologists have developed many models of graph comprehension, most of them descriptive, some computational. We map the descriptive models into requirements for a cognitive architecture that can be used to build predictive computational models. General symbolic architectures such as Act-R and Soar satisfy the requirements except for those to support mental imagery operations required for many...

(3) Soar does not currently include any capacity limits on its dynamic memory (SDM), but is compatible with certain such limitations. In particular, a constraint that SDM can hold at most two items of the same “type” (suitably defined) yields a coherent explanation for many psycholinguistic phenomena in the comprehension of sentences. This constraint is motivated by computational efficiency con...

Existing computational models of emotion are primarily concerned with creating more realistic agents, with recent efforts looking into matching human data, including qualitative emotional responses and dynamics. In this paper, our work focuses on the functional benefits of emotion in a cognitive system where emotional feedback helps drive reinforcement learning. Our system is an integration of ...

Remarkable and impressive advancements in the areas of perception, mapping and navigation of artificial mobile systems have been witnessed in the last decades. However, it is clear that important limitations remain regarding the spatial cognition capabilities of existing available implementations and the current practical functionality of high level cognitive models [1, 2]. For enhanced robustn...

Action modeling is an important skill for agents that must perform tasks in novel domains. Previous work on action modeling has focused on learning STRIPS operators in discrete, relational domains. There has also been a separate vein of work in continuous function approximation for use in optimal control in robotics. Most real world domains are grounded in continuous dynamics but also exhibit e...

Cognitive architectures are theories of cognition that try to capture the essential representations and mechanisms that underlie cognition. Research in cognitive architectures has gradually moved from a focus on the functional capabilities of architectures to the ability to model the details of human behavior, and, more recently, brain activity. Although there are many different architectures, ...

This paper explores how a general cognitive architecture can pragmatically facilitate the development and exploration of interactive music interfaces on a mobile platform. To this end we integrated the Soar cognitive architecture into the mobile music meta-environment urMus. We develop and demonstrate four artificial agents which use diverse learning mechanisms within two mobile music interface...