The goal of our work is to develop architectures for general intelligent agents that can use large bodies of knowledge to achieve a variety of goals in realistic environments. Our e orts to date have been realized in the Soar architecture. In this paper we provide an overview of plan execution in Soar. Soar is distinguished by its use of learning to compile planning activity automatically into ...

Discussed in greater detail below, Soar-RL is the integration of the reinforcement learning method of machine learning into Soar, a generalized architecture. The MAXQ method for hierarchical reinforcement learning [1] greatly influenced the design for the hierarchical reinforcement learning components of Soar-RL [2]. In its pre-release form, it is prudent to question the merits of this union: w...

Developing and testing intelligent agents is a complex task that is both time-consuming and costly. This creates the potential that problems in the agent’s behavior will be realized only after the agent has been put to use. In this paper we explore two implementations of a generic agent selfassessment framework applied to the Soar agent architecture. Our system extends previous work and can be ...

The goal of the Soar/IFOR project is to provide intelligent agents capable of replacing human agents in large-scale distributed military simulations and smallscale, focused training exercises. The need for computer generated agents to remain reactive determines the requirements of the NL capability: (1) it nmst occur ill real time, (2) it nmst seamlessly integrate with the agent’s non-linguisti...