People acquire new knowledge in various ways and this helps them to adapt to changing environment properly. In this paper, we investigatethe interoperation of multiple learning mechanisms within a single system. We extend a cognitive architecture, ICARUS, to have three different modes of learning. Through experiments in a modified Blocks World and a route generation domain, we test and demonstrate the system's ability to get synergistic effects from these learning mechanisms.

In this paper, we address the problem of communicating, interpreting,and executing complex yet abstract instructions to a robot teammember. This requires specifying the tasks in an unambiguous manner,translating them into operational procedures, and carrying outthose procedures in a persistent yet reactive manner. We reportour response to these issues, after which we demonstrate theircombined use in controlling a mobile robot in a multi-room officesetting on tasks similar to those in search-and-rescue operations.We conclude by discussing related research and suggesting directionsfor future work.

In this paper, we report progress on making synthetic characters more taskable. In particular, we present an English-like command language that lets one specify complex behaviors an agent should carry out in a virtual environment. We also report compilers that translate English commands into a formal notation and formal statements into procedures for Icarus, an agent architecture that supports reactive execution. To demonstrate the benefits of such taskability, we have integrated Icarus with Twig, which provides a simulated physical environment with humanoid agents. We use the command language to specify three complex activities, including responding to an object contingently, collecting and storing a set of objects, and negotiating with another agent in order to purchase an item. We also discuss related work on controlling synthetic characters, along with paths for additional research on taskability.

In this article, I claim that research on cognitive architectures is an important path to the development of general intelligent systems. I contrast this paradigm with other approaches to constructing such systems, and I review the theoretical commitments associated with a cognitive architecture. I illustrate these ideas using a particular architecture -- ICARUS -- by examining its claims about memories, about the representation and organization of knowledge, and about the performance and learning mechanisms that affect memory structures. I also consider the high-level programming language that embodies these commitments, drawing examples from the domain of in-city driving. In closing, I consider ICARUS's relation to other cognitive architectures and discuss some open issues that deserve increased attention.