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Artificial Consciousness in Control Systems

Profile image of Ricardo SanzRicardo Sanz

1995

Abstract

Abstract: Control systems are gaining in complexity to handle complex problems in uncertain conditions. Artificial Intelligence technology has served well to handle concrete problems in local scopes, but recent trends in controller construction lead to the need of plant-wide integrated systems in order to maximize efficiency. Surprisingly, Integrated Intelligent Controllers are reaching complexity level and behavioral features that touch that old big dream of the AI community: the conscious machine.

FAQs

sparkles

AI

What explains the need for a new control theory in intelligent systems engineering?add

The paper reveals that traditional control theory is lagging behind advancements in AI and robotics, necessitating a sound mind theory for improved functionality and complexity management in control systems.

How do intelligent systems architecture affect control systems' functionality?add

Architecture-centric development processes have shown effectiveness in managing complexity and enhancing functionality, particularly in autonomous systems like flight control and nuclear safety.

What role does self-awareness play in intelligent control systems?add

The study indicates that self-awareness allows intelligent systems to maintain an integrated model for decision-making, improving performance under uncertainty and resource scarcity.

How does consciousness potentially enhance the performance of artificial control systems?add

Research suggests conscious control systems could outperform unconscious ones by enabling better monitoring and adaptation, particularly in critical applications such as intensive care and flight control.

When did consciousness emerge as a concept in artificial control systems research?add

The emergence of consciousness as a consideration in artificial systems has gained momentum recently, facilitated by cross-disciplinary insights from psychology and cognitive science.

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This paper discusses the psychological, philosophical and neurological definitions of consciousness and the prospects for the development of a conscious machine in the foreseeable future. Various definitions of consciousness are introduced and discussed within the different fields mentioned. We conclude that a conscious machine may be within the realm of engineering possibilities if current technological developments, especially Moore's Law, continue at their current pace. Given the complexity of cognition and consciousness a hybrid architecture appears to offer the best solution for the implementation of a complex system of systems which functionally approximates a human mind. Ideally, this architecture would include traditional symbolic representations as well as distributed representations which approximate the nonlinear dynamics seen in the human brain.

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Thus far, we have experienced three artificial intelligence (AI) booms. In the third one, we succeeded in developing AI that partially surpassed human capabilities. However, we are yet to develop AI that, like humans, can perform a series of cognitive processes. Consciousness built into devices is called machine consciousness. Related research has been conducted from two perspectives: studying machine consciousness as a tool to elucidate human consciousness and achieving the technological goal of furthering AI research with conscious AI. Herein, we survey the research conducted on machine consciousness from the second perspective. For AI to attain machine consciousness, its implementation must be evaluated. Therefore, we only surveyed attempts to implement consciousness as systems on devices. We collected research results in chronological order and found no breakthroughs that could deliver machine consciousness soon. Moreover, there is no method to evaluate whether an implemented machine consciousness system possesses consciousness, thus making it difficult to confirm the certainty of the implementation. This field of research is a new frontier. It is an exciting field with many discoveries expected in the future.

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References (15)

  1. Kuo, B.: Automatic Control Systems. Prentice-Hall, Upper Saddle River, NJ (1991)
  2. Franklin, S.P.: Artificial Minds. MIT Press, Cambridge, MA (1995)
  3. Antsaklis, P., Passino, K.E.: An Introduction to Intelligent and Autonomous Control. Kluwer Academic Publishers (1993)
  4. Sanz, R., Schaufelberger, W., Pfister, C., de Antonio, A.: Software for complex control systems. In str öm, K.A., Albertos, P., Blanke, M., Isidori, A., Schaufelberger, W., Sanz, R., eds.: Control of Complex Systems. Springer (2000)
  5. Garlan, D., Perry, D.E.: Introduction to the special issue on software architecture. IEEE Transactions on Software Engineering 21 (1995) 269-274
  6. Buschmann, F., Meunier, R., Rohnert, H., Sommerlad, P., Stal, M.: Pattern Oriented Software Architecture. A System of Patterns. John Wiley and Sons, New York (1996)
  7. Meystel, A., Messina, E.: The challenge of intellligent systems. In: Proceedings of ISIC'2000, 15th IEEE International Symposium on Intelligent Control, Rio, Patras, Greece (2000)
  8. Chalmers, D.J.: The Conscious Mind. Philosophy of Mind. Oxford University Press, Oxford-New York (1996)
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  10. Albus, J.S., Meystel, A.M.: Engineering of Mind: An Introduction to the Science of Intelligent Systems. John Wiley & Sons Inc. (2001)
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Abstract—This work describes the application of the Baars-Franklin Architecture (BFA), an artificial consciousness approach, to synthesize a control system for an autonomous vehicle. The BFA was reported in the literature as a successful control system to different kinds of agents: CMattie, IDA and CTS. In this paper, BFA is for the first time applied for controlling an autonomous vehicle. Firstly we introduce the theoretical foundations of this approach for the development of a conscious agent.

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