One challenge for cognitive architectures is to effectively use different forms of knowledge and learning. We present a case study of Soar agents that play a multiplayer dice game, in which probabilistic reasoning and heuristic symbolic knowledge appear to play a central role. We develop and evaluate a collection of agents that use different combinations of probabilistic decision making, heuristic symbolic reasoning, opponent modeling, and learning. We demonstrate agents that use Soar’s rule learning mechanism (chunking) to convert deliberate reasoning with probabilities into implicit reasoning, and then use reinforcement learning to further tune performance.

In order to collaborate with people in the real world, cognitive systems must be able to represent and reason about spatial regions in human environments. Consider the command "go to the front of the classroom". The spatial region mentioned (the front of the classroom) is not perceivable using geometry alone. Instead it is defined by its functional use, implied by nearby objects and their configuration. In this paper, we define such areas as context-dependent spatial regions and propose a method for a cognitive system to learn them incrementally by combining qualitative spatial representations, semantic labels, and analogy. Using data from a mobile robot, we generate a relational representation of semantically labeled objects and their configuration. Next, we show how the boundary of a context-dependent spatial region can be defined using anchor points. Finally, we demonstrate how an existing computational model of analogy can be used to transfer this region to a new situation.

Computationally hard problems, like the Traveling Salesperson Problem, can be solved remarkably well by humans. Results obtained by computers are usually closer to the optimum, but require high computational effort and often differ from the human solutions. This paper introduces Greedy Expert Search (GES) that strives to show the same flexibility and efficiency of human solutions, while producing results of similarly high quality. The Traveling Salesperson Problem serves as an example problem to illustrate and evaluate the approach.

We describe a cognitive model of a collaborative agent that can serve as the basis for automated systems that must collaborate with other agents, including humans, to solve problems. This model builds on standard approaches to cognitive architecture and intelligent agency, as well as formal models of speech acts, joint intention, and intention recognition. The model is nonetheless intended for practical use in the development of collaborative systems.

Scone is a knowledge-base system developed specifically to support human-like common-sense reasoning and the understanding of human language. One of the unusual features of Scone is its multiple-context system. Each context represents a distinct world-model, but a context can inherit most of the knowledge of another context, explicitly representing just the differences. We explore how this multiple-context mechanism can be used to emulate some aspects of human mental behavior that are difficult or impossible to emulate in other representational formalisms. These include reasoning about hypothetical or counter-factual situations; understanding how the world model changes over time due to specific actions or spontaneous changes; and reasoning about the knowledge and beliefs of other agents, and how their mental state may affect the actions of those agents.

As people engage in increasingly complex conversations with computers, the need for generality and flexibility in spoken dialogue systems becomes more apparent. This pa­per describes how three different spoken dialogue systems for the same task reason with knowledge and certainty as they seek to understand what people want. It advocates sys­tems that exploit partial understanding, consider credibility, and are aware both of what they know and of their certainty that it matches their users’ intent.

This paper documents a functionality-driven exploration of automatic working-memory management in Soar. We first derive and discuss desiderata that arise from the need to embed a mechanism for managing working memory within a general cognitive architecture that is used to develop real-time agents. We provide details of our mechanism, including the decay model and architecture-independent data structures and algorithms that are computationally efficient. Finally, we present empirical results, which demonstrate both that our mechanism performs with little computational overhead and that it helps maintain the reactivity of a Soar agent contending with long-term, autonomous simulated robotic exploration as it reasons using large amounts of acquired information.

Researchers have studied problems in metacognition both in computers and in humans. In response some have implemented models of cognition and metacognitive activity in various architectures to test and better define specific theories of metacognition. However, current theories and implementations suffer from numerous problems and lack of detail. Here we illustrate the problems with two different computational approaches. The Meta-Cognitive Loop and Meta-AQUA both examine the metacognitive reasoning involved in monitoring and reasoning about failures of expectations, and they both learn from such experiences. But neither system presents a full accounting of the variety of known metacognitive phenomena, and, as far as we know, no extant system does. The problem is that no existing cognitive architecture directly addresses metacognition. Instead, current architectures were initially developed to study more narrow cognitive functions and only later were they modified to include higher level attributes. We claim that the solution is to develop a metacognitive architecture outright, and we begin to outline the structure that such a foundation might have.

Social cognition is a key feature of human-level intelligence. However, social reasoning faculties are rarely included in cognitive systems. To encourage research in this direction, we introduce a practical, computational framework that enables socially aware inference. We demonstrate the framework's ability to model a common, complex, and under-investigated aspect of human social behavior: deception. Moreover, we show how a system implementing this framework could dynamically respond once it has detected a lie. We then discuss some of the challenges associated with deception, ending with an outline of future research directions.

In the context of cognitive architectures, memory is typically considered as a passive storage device with the sole purpose of maintaining and retrieving information relevant to ongoing cognitive processing. If memory is instead considered to be a fundamentally active aspect of cognition, as increasingly suggested by empirically-derived neurophysiological theory, this passive role must be reinterpreted. In this perspective, memory is the distributed substrate of cognition, forming the foundation for cross-modal priming, and hence soft cross-modal coordination. This paper seeks to describe what a cognitive architecture based on this perspective must involve, and initiates an exploration into how human-level cognitive competencies (namely episodic memory, word label conjunction learning, and social behaviour) can be accounted for in such a low-level framework. This proposal of a memory-centred cognitive architecture presents new insights into the nature of cognition, with benefits for computational implementations such as generality and robustness that have only begun to be exploited.