Artificial Minds with Consciousness and Common sense Aspects:

Resource type
Journal Article
Authors/contributors
Title
Artificial Minds with Consciousness and Common sense Aspects:
Abstract
The research work presented in this article investigates and explains the conceptual mechanisms of consciousness and common-sense thinking of animates. These mechanisms are computationally simulated on artificial agents as strategic rules to analyze and compare the performance of agents in critical and dynamic environments. Awareness and attention to specific parameters that affect the performance of agents specify the consciousness level in agents. Common sense is a set of beliefs that are accepted to be true among a group of agents that are engaged in a common purpose, with or without self-experience. The common sense agents are a kind of conscious agents that are given with few common sense assumptions. The so-created environment has attackers with dependency on agents in the survival-food chain. These attackers create a threat mental state in agents that can affect their conscious and common sense behaviors. The agents are built with a multi-layer cognitive architecture COCOCA (Consciousness and Common sense Cognitive Architecture) with five columns and six layers of cognitive processing of each precept of an agent. The conscious agents self-learn strategies for threat management and energy level maintenance. Experimentation conducted in this research work demonstrates animate-level intelligence in their problem-solving capabilities, decision making and reasoning in critical situations.
Publication
International Journal of Agent Technologies and Systems
Volume
9
Issue
1
Pages
20-42
Date
01/2017
Language
en
ISSN
1943-0744, 1943-0752
Short Title
Artificial Minds with Consciousness and Common sense Aspects
Accessed
3/7/25, 7:13 AM
Library Catalog
DOI.org (Crossref)
Citation
Shylaja, K. R., Vijayakumar, M. V., Prasad, E. V., & Davis, D. N. (2017). Artificial Minds with Consciousness and Common sense Aspects: International Journal of Agent Technologies and Systems, 9(1), 20–42. https://doi.org/10.4018/IJATS.2017010102