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Artificial Minds with Consciousness and Common sense Aspects

K. R. Shylaja, M.V. Vijayakumar, E. Vani Prasad, Darryl N. Davis

International Journal of Agent Technologies and Systems 2017 DOI: 10.4018/ijats.2017010102 (opens in new tab)

Study at a glance

AI-extracted from the abstract
Characteristics Theoretical or philosophical paper Peer reviewed
Population Artificial agents simulated in a computational environment
Citations 3
Key points The authors propose that consciousness and common-sense thinking can be computationally simulated in artificial agents using a multi-layer cognitive architecture (COCOCA) with five columns and six layers. They report that their experiments show animate-level intelligence in the agents' problem-solving, decision-making, and reasoning under threat, and argue that awareness and attention to specific parameters specify consciousness levels.

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.

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