Conditional Inference and Activation of Knowledge Entities in ACT-R

10/28/2021
by   Marco Wilhelm, et al.
FernUniversität in Hagen
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/25/2020

Non-Destructive Sample Generation From Conditional Belief Functions

This paper presents a new approach to generate samples from conditional ...
01/29/2022

On Polynomial Approximation of Activation Function

In this work, we propose an interesting method that aims to approximate ...
02/11/2022

Inference with System W Satisfies Syntax Splitting

In this paper, we investigate inductive inference with system W from con...
03/13/2018

Conditional Activation for Diverse Neurons in Heterogeneous Networks

In this paper, we propose a new scheme for modelling the diverse behavio...
03/27/2013

Probabilistic Reasoning About Ship Images

One of the most important aspects of current expert systems technology i...
02/06/2013

Computational Advantages of Relevance Reasoning in Bayesian Belief Networks

This paper introduces a computational framework for reasoning in Bayesia...

Please sign up or login with your details

Forgot password? Click here to reset