Artificial Persuasion in Pedagogical Games

01/23/2016
by   Zhiwei Zeng, et al.
0

A Persuasive Teachable Agent (PTA) is a special type of Teachable Agent which incorporates a persuasion theory in order to provide persuasive and more personalized feedback to the student. By employing the persuasion techniques, the PTA seeks to maintain the student in a high motivation and high ability state in which he or she has higher cognitive ability and his or her changes in attitudes are more persistent. However, the existing model of the PTA still has a few limitations. Firstly, the existing PTA model focuses on modelling the PTA's ability to persuade, while does not model its ability to be taught by the student and to practice the knowledge it has learnt. Secondly, the quantitative model for computational processes in the PTA has low reusability. Thirdly, there is still a gap between theoretical models and practical implementation of the PTA. To address these three limitations, this book proposes an improved agent model which follows a goal-oriented approach and models the PTA in its totality by integrating the Persuasion Reasoning of the PTA with the Teachability Reasoning and the Practicability Reasoning. The project also proposes a more abstract and generalized quantitative model for the computations in the PTA. With higher level of abstraction, the reusability of the quantitative model is also improved. New system architecture is introduced to bridge the gap between theoretical models and implementation of the PTA.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/17/2023

Probabilistic Results on the Architecture of Mathematical Reasoning Aligned by Cognitive Alternation

We envision a machine capable of solving mathematical problems. Dividing...
research
01/30/2022

Computational Metacognition

Computational metacognition represents a cognitive systems perspective o...
research
06/16/2023

Coaching a Teachable Student

We propose a novel knowledge distillation framework for effectively teac...
research
08/21/2023

Neural Amortized Inference for Nested Multi-agent Reasoning

Multi-agent interactions, such as communication, teaching, and bluffing,...
research
10/12/2021

AVoE: A Synthetic 3D Dataset on Understanding Violation of Expectation for Artificial Cognition

Recent work in cognitive reasoning and computer vision has engendered an...
research
09/26/2016

Implementing RBAC model in An Operating System Kernel

In this paper, the implementation of an operating system oriented RBAC m...
research
08/12/2021

A Mathematical Approach to Constraining Neural Abstraction and the Mechanisms Needed to Scale to Higher-Order Cognition

Artificial intelligence has made great strides in the last decade but st...

Please sign up or login with your details

Forgot password? Click here to reset