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...

We first introduce a second-order Krylov subspace Gn(A,B;u) based on a pair of square matrices A and B and a vector u. The subspace is spanned by a sequence of vectors defined via a second-order linear homogeneous recurrence relation with coefficient matrices A and B and an initial vector u. It generalizes the well-known Krylov subspace Kn(A;v), which is spanned by a sequence of vectors defined...

Ca(2+) influx by store-operated Ca(2+) channels (SOCs) mediates all Ca(2+)-dependent cell functions, but excess Ca(2+) influx is highly toxic. The molecular components of SOC are the pore-forming Orai1 channel and the endoplasmic reticulum Ca(2+) sensor STIM1. Slow Ca(2+)-dependent inactivation (SCDI) of Orai1 guards against cell damage, but its molecular mechanism is unknown. Here, we used hom...

This report describes a theory of garden path phenomena that is emerging from work on NL-Soar, a computational model of language comprehension embedded within the Soar architecture. The theory is constrained by a corpus of two kinds of sentences: garden paths (GP), which reveal the limitations of human comprehension in dealing with local ambiguities, and non-garden-paths (NGP), which reveal its...