Conditional Inference and Activation of Knowledge Entities in ACT-R

TL;DR

This paper introduces an activation-based conditional inference method for ACT-R that models human reasoning by selecting relevant beliefs based on activation levels, integrating focus, forgetting, and remembering into expert systems.

Contribution

It presents a novel activation function for conditional inference in ACT-R, enabling more human-like reasoning by dynamically managing belief relevance and memory.

Findings

Effective modeling of human reasoning processes

Improved inference efficiency in ACT-R

Integration of cognitive aspects like focus and forgetting

Abstract

Activation-based conditional inference applies conditional reasoning to ACT-R, a cognitive architecture developed to formalize human reasoning. The idea of activation-based conditional inference is to determine a reasonable subset of a conditional belief base in order to draw inductive inferences in time. Central to activation-based conditional inference is the activation function which assigns to the conditionals in the belief base a degree of activation mainly based on the conditional's relevance for the current query and its usage history. Therewith, our approach integrates several aspects of human reasoning into expert systems such as focusing, forgetting, and remembering.