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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Opportunistic Behavior in Motivated Learning Agents.

James Graham, Janusz A Starzyk, Daniel Jachyra

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    This summary is machine-generated.

    This study introduces motivated learning (ML) combined with opportunistic behavior for intelligent agents. Opportunistic agents outperform standard ML agents by optimizing all needs, enhancing real-time robotic learning in complex environments.

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    Area of Science:

    • Artificial Intelligence
    • Robotics
    • Machine Learning

    Background:

    • Intelligent agents require advanced learning mechanisms for dynamic environments.
    • Existing motivated learning (ML) schemes can be extended to incorporate opportunistic behaviors.
    • Autonomous systems need to adapt and exploit opportunities for improved performance.

    Purpose of the Study:

    • To develop and implement a novel motivated learning (ML) scheme incorporating opportunistic behavior in intelligent agents.
    • To demonstrate the effectiveness of autonomous opportunistic agents in a virtual, dynamically changing environment.
    • To compare the performance of opportunistic agents against agents relying solely on ML.

    Main Methods:

    • Virtual world implementation of autonomous agents.
    • Development of a motivated learning (ML) scheme integrated with opportunistic strategies.
    • Agents learn to create abstract goals and exploit emergent opportunities.
    • Performance evaluation based on minimizing average need signals versus a single dominating need.

    Main Results:

    • Opportunistic agents demonstrated superior performance compared to agents using ML exclusively.
    • The strategy of minimizing the average value of all need signals proved more effective.
    • Agents successfully learned to adapt, set abstract goals, and leverage opportunities in a dynamic environment.

    Conclusions:

    • Combining motivated learning (ML) with opportunistic behavior enhances agent performance in complex, dynamic environments.
    • This approach is applicable to the real-time learning and operation of autonomous embodied systems, such as robots.
    • Opportunistic agents offer a more robust and adaptive learning paradigm for real-world applications.