If the laws of percepts change, which are described by deterministic Markov decision process, exist in the observable or partially observable environment, then the agent by interacting with the environment and having no initial knowledge is capable to discover those laws using the methods of logical and constructive induction (i.e. is capable to learn environment and goal models); it is capable to learn predicting precisely its own consequences of actions and apply this learned knowledge in order to achieve its own goals in the new unseen situations. Adaptive agent proposed in this dissertation differs from other similar works presented in the literature in three novel potentials that enables this agent to solve the problem of knowledge transferability from one environment into another, when the same laws are valid for the environments; to solve the problem of goal percepts generalization; and to solve the problem of perceptual aliasing in the partially observable environment. During the investigations it was discovered that adaptive agent using created environment model solves the tasks of knowledge transferability into new environments better than other alternative agents (based on Q-learning and ADP methods); using created goal model solves the goal percepts generalization tasks by correctly approximating the reward function and predicting its values in the new environments; solves the problem of perceptual aliasing by transforming the deterministic nth order Markov decision process into the 1st order and by creating environment model that corresponds to finite Moore automaton.


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    Title :

    Adaptyvaus agento aplinkos ir tikslo modelių indukcija deterministinėje aplinkoje ; Induction of environment and goal models by an adaptive agent in deterministic environment



    Publication date :

    2011-01-24


    Type of media :

    Theses


    Type of material :

    Electronic Resource


    Language :

    Lithuanian , English



    Classification :

    DDC:    629