Introduction to Soar

05/08/2022
by   John E. Laird, et al.
16

This paper is the recommended initial reading for a functional overview of Soar, version 9.6. It includes an abstract overview of the architectural structure of Soar including its processing, memories, learning modules, their interfaces, and the representations of knowledge used by those modules. From there it describes the processing supported by those modules, including decision making, impasses and substates, procedure learning via chunking, reinforcement learning, semantic memory, episodic memory, and spatial-visual reasoning. It then reviews the levels of decision making and variety of learning in Soar, and analysis of Soar as an architecture supporting general human-level AI. Following the references is an appendix that contains short descriptions of recent Soar agents and a glossary of the terminology we use in describing Soar.

READ FULL TEXT
research
04/14/2019

A Short Survey On Memory Based Reinforcement Learning

Reinforcement learning (RL) is a branch of machine learning which is emp...
research
02/17/2022

A Survey of Explainable Reinforcement Learning

Explainable reinforcement learning (XRL) is an emerging subfield of expl...
research
06/09/2023

Decision Stacks: Flexible Reinforcement Learning via Modular Generative Models

Reinforcement learning presents an attractive paradigm to reason about s...
research
06/29/2022

Debiasing architectural decision-making: a workshop-based training approach

Cognitive biases distort the process of rational decision-making, includ...
research
07/02/2023

Minimum Levels of Interpretability for Artificial Moral Agents

As artificial intelligence (AI) models continue to scale up, they are be...
research
08/25/2023

MRNAV: Multi-Robot Aware Planning and Control Stack for Collision and Deadlock-free Navigation in Cluttered Environments

Multi-robot collision-free and deadlock-free navigation in cluttered env...
research
05/12/2019

Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations

Assemblies of modular subsystems are being pressed into service to perfo...

Please sign up or login with your details

Forgot password? Click here to reset