Much of the work in machine learning has focused on situations in which there are distinct training and testing phases. However, the recent increase in interest in real-world systems involving scaling up has led to an increased attention to “anytime learning”, i.e., systems in which learning mechanisms are always active. This places different demands on machine learning techniques and raises a ...

Endowing an intelligent agent with an episodic memory affords it a multitude of cognitive capabilities. However, providing efficient storage and retrieval in a task-independent episodic memory presents considerable theoretical and practical challenges. We characterize the computational issues bounding an episodic memory. We explore whether even with intractable asymptotic growth, it is possible...

This paper describes a BDI-inspired abstraction layer that is implemented in Soar to increase the scalability and flexibility of knowledge engineering in Soar-based agent systems. We motivate the BDI abstraction with a specific example of a multi-agent system that is being developed to explore human command and control of heterogeneous robotic entities. We then describe how we have implemented ...

The characteristics of long-term, symbolic learning were investigated using Soar and ACT-R models of a task to rearrange blocks into specific configurations. Long sequences of problems were run collecting data to answer fundamental questions about long-term, symbolic learning. The questions were whether symbolic learning continues indefinitely, how learned knowledge is used, and whether perform...

This paper describes the development of the Cognitive Robotic System (CRS) for studying intelligent and autonomous unmanned vehicles. This system uses a multi-threaded software system written using Java to integrate the Soar cognitive architecture with a sixlegged mobile robot and its sensors and motors. The results of experiments in which the Cognitive Robotic System was used to control the he...

The Quadratic Arnoldi algorithm is an Arnoldi algorithm for the solution of the quadratic eigenvalue problem, that exploits the structure of the Krylov vectors. This allows us to reduce the memory requirements by about a half. The method is an alternative to the Second Order Arnoldi method (SOAR). In the SOAR method it is not clear how to perform an implicit restart. We discuss various choices ...

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 realtime agents. We provide details of our mechanism, including the decay model and architecture-independent data st...

Challenging scholarly articles are not easy to read and understand by learners. Our previous work shows an innovative learning framework –SOAR (Scholarly Article) validated on a group of learners without research experience on improving their comprehension skill on scholarly articles. In this paper, we present additional result after validating SOAR on novice learners who first time read schola...