Development and Inhibition of Learning Abilities in Agents and Intelligent Systems

Abstract

In human social life, ability to learn faster than your competitors is sustainable competitive advantage (de Geus). This statement may concern not only humans, but also artificial intelligence systems, learning ability of which is considered a most important feature by most researchers. Yet a general law of competition is that a participant of competition can gain competitive advantages by two ways: (a) increase of its own potential, and (b) premeditated decrease of competitors' potential. So, paradoxically, possible directions of artificial intelligence systems development can be design of systems that are able to: (a) counteract other systems' learning, decrease their learning abilities and conduct their teaching; (b) learn and increase of their leaning abilities and general intellectual level in conditions of counteraction to their learning. In the paper, distinguishing between control of learning and control of learning ability is introduced. An approach to construction of models of the learning ability control and of agents' mutual teaching/learning is described. Effects of unpremeditated and premeditated Trojan horse teaching in agents' interactions are discussed. The aim of future researches is design of competitive environments, in which struggle for higher levels of learning abilities is presented explicitly as a key parameter and which provide with an opportunity to generate and select the agents with maximal learning abilities.