This paper outlines a specification for an algorithm-design system (based on previous work involving protocol analysis) and describes an implementation of the specification that is a combination frame and production system. In the implementation, design occurs in two problem spaces -- one about algorithms and one about the task-domain. The partially worked out algorithms are represented as configurations of dataflow components. A small number of general-purpose operators construct and modify the representations. These operators are adapted to different situations by instantiation and means-ends ana,lysis rules. The data-flow space also includes symbolic and test-case execution rules that drive the component-refinement orocess by exposing both problems and opportunities. A domain space about geometric images supports test,case execution, domain-specific problem solving, recognition and discovery.
I propose that the notion of cognitive state be broadened from the current predicate-symbolic, Language-of-Thought framework to a multi-modal one, where perception and kinesthetic modalities participate in thinking. In contrast to the roles assigned to perception and motor activities as modules external to central cognition in the currently dominant theories in AI and Cognitive Science, in the proposed approach, central cognition incorporates parts of the perceptual machinery. I motivate and describe the proposal schematically, and describe the implementation of a bimodal version in which a diagrammatic representation component is added to the cognitive state. The proposal explains our rich multimodal internal experience, and can be a key step in the realization of embodied agents. The proposed multimodal cognitive state can significantly enhance the agent's problem solving. Note: Memory, as well as the information retrieved from memory and from perception, represented in a predicate-symbolic form.
Traditionally, a decision support system is built to help a broad range of users in their decision making. However, there are some domains such as air traffic control, the nuclear industry, and certain military tasks, where it is critical that particular individuals make correct judgements for the safety of themselves and others. For such people, it is possible that a decision support system tailored to their own individual way of working can help them in their decision making tasks. Decision makers in such environments can be viewed as using naturalistic decision making methods, and cognitive models of such people performing a simulated electronic warfare task have been implemented using the Soar architecture, for inclusion in a decision support system. This task was chosen because it displays the characteristics most relevant to naturalistic decision making, namely rapid decision making in complex, real-time environments.
We describe a generic approach for modeling the impact of emotion on cognition, perception, and behavior. The approach can model the effects of transient emotional states, longer moods, and stable personality and temperamental factors. The underlying assumption is that one of the primary ways in which emotions influence cognition and perception is by modulating a variety of processing parameters. We illustrate the approach in the context of both a generic integrated architecture of cognition, and a specific architecture, currently under development, designed to model decision making behavior. In this context, we illustrate how the approach would be instantiated within several representational formalisms (e.g., rules, belief nets). We focus on modeling the impact on tactical decision-making of three specific emotional states that have been studied extensively in experimental psychology: anxiety, negative affect (e.g., depression), and obsessiveness. The proposed approach can then be used both for investigating the interaction between cognition and emotion, and the resulting behavior, and for modeling specific types of personalities in interactive environments.