Natural language is a uniquely convenient means of communication due to, among its other properties, its flexibility and its openness to interpretation. These properties of natural language are largely made possible by its heavy dependence on context and common ground. Drawing on elements of Clark’s account of language use, we view natural language interactions as a coordination problem involving agents who work together to convey and thus coordinate their interaction goals. In the modeling work presented here, a sequence of interrelated modules developed in the Polyscheme cognitive architecture is used to implement several stages of reasoning the user of a simple video application would expect an addressee—ultimately, the application—to work through, if the interaction goal was to locate a scene they had previously viewed together.

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.

This paper discusses the challenge of designing instructable agents that can learn through interaction with a human expert. Learning through instruction is a powerful paradigm for acquiring knowledge because it limits the complexity of the learning task in a variety of ways. To support learning through instruction, the agent must be able to effectively communicate its lack of knowledge to the human, comprehend instructions, and apply them to the ongoing task. Weidentify some problems of concern when designing instructable agents. We propose an agent design that addresses some of these problems. We instantiate this design in the Soar cognitive architecture and analyze its capabilities on a learning task.

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.

The Betty’s Brain computer-based learning system provides an open-ended and choice-rich environment for science learning. Using the learning-by-teaching paradigm paired with feedback and support provided by two pedagogical agents, the system also promotes the development of self-regulated learning strategies to support preparation for future learning. We apply metacognitive learning theories and experiential analysis to interpret the results from previous classroom studies. We propose an integrated cognitive and metacognitive model for effective, self-regulated student learning in the Betty’s Brain environment, and then apply this model to interpret and analyze common suboptimal learning strategies students apply during their learning. This comparison is used to derive feedback for helping learners overcome these difficulties and adopt more effective strategies for regulating their learning. Preliminary results demonstrate that students who were responsive to the feedback had better learning performance.

Despite recent impressive progress in automated planning and navigation tools, artifacts still lack robustness and flexibility of biological systems. In order to mimic biology, it is necessary to use principles of dynamics and architecture found in the brain. Here we translate our biologically inspired model of spatial learning and navigation (Samsonovich and Ascoli, L&M 2005) into a model suitable for implementation in spiking networks with STDP synapses, based on soon to become available hardware. Simulation studies of the model prove its robustness and scalability. The approach naturally extends to various types of action planning beyond the spatial domain. The architecture can be used in autonomous intelligent agents of various nature.

Understanding the interaction between emotion and cognitive processes is important for developing architectures for general intelligence, and vital for the fields of human social and behavioral modeling, game intelligence, and human-computer interaction. However, relatively little work in AI has been done on emotion in intelligent architectures, particularly on the effect of emotions on cognitive processes such as inference, planning and learning, despite research showing that emotion is a crucial and often beneficial factor in human decision-making. My work will provide a new emotional-cognitive architecture, focusing on a small set of theories, mechanisms and algorithms for the modeling of a wide array of emotional effects on human cognitive processes. The work and its results will be evaluated against current computational models of cognition and emotion, and validated by results from human cognitive science, neuroscience, and psychology.

Effective access to knowledge within large declarative memory stores is one challenge in the development and understanding of long-living, generally intelligent agents. We focus on a sub-component of this problem: given a large store of knowledge, how should an agent's task-independent memory mechanism respond to an ambiguous cue, one that pertains to multiple previously encoded memories. A large body of cognitive modeling work suggests that human memory retrievals are biased in part by the recency and frequency of past memory access. In this paper, we evaluate the functional benefit of a set of memory retrieval heuristics that incorporate these biases, in the context of the word sense disambiguation task, in which an agent must identify the most appropriate word meaning in response to an ambiguous linguistic cue. In addition, we develop methods to integrate these retrieval biases within a task-independent declarative memory system implemented in the Soar cognitive architecture and evaluate their effectiveness and efficiency in three commonly used semantic concordances.

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 emergent regularities at an abstract relational level of description. These two levels of regularity are often difficult to capture using a single action representation and learning method. In this paper we describe a system that combines discrete and continuous action modeling techniques in the Soar cognitive architecture. Our system accepts a continuous state representation from the environment and derives a relational state on top of it using spatial relations. The dynamics over each representation is learned separately using two simple instance-based algorithms. The predictions from the individual models are then combined in a way that takes advantage of the information captured by each representation. We empirically show that this combined model is more accurate and generalizable than each of the individual models in a spatial navigation domain.

The development of service robots has gained more and more attention over the last years. Advanced robots have to cope with many different situations and contingencies while executing concurrent and interruptable complex tasks. To manage the sheer variety of different execution variants the robot has to decide at run-time for the most appropriate behavior to execute. That requires task coordination mechanisms that provide the flexibility to adapt at run-time and allow to balance between alternatives.