Anticipatory Behavior in Adaptive Learning Systems
Developing a consciousness-based mind for an artificial creature
SBIA'10 Proceedings of the 20th Brazilian conference on Advances in artificial intelligence
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Despite impressive advances in the past decades, autonomous agents living in dynamic and unpredictable environments are typically equipped with simple decision-making mechanisms in their sense-decide-act routines. These agents deal mostly with one goal at a time. This research aspires to model, design and/or implement a sophisticated decision making mechanism that selects the agent's next action with different levels of awareness: automatized skills, consciously mediated routine solutions, and consciously deliberated non-routine solutions. Such a decision-making mechanism is presented in a "conscious" software agent framework called IDA that implements Baars' Global Workspace Theory of consciousness. IDA integrates many computational and conceptual mechanisms, among which this research deals with its action selection, expectation, automatization and non-routine problem solving modules. The overarching continual task of an agent's intelligence is for the service of choosing, at each moment in time, the appropriate action in response to exogenous and endogenous stimuli. IDA's action selection mechanism (ASM) can interleave and prioritize actions of different and competing goal hierarchies. The ASM system is implemented as a domain independent and reusable framework for behavior networks and is tested as a controller to a khepera robot operating in a real world domain. We humans have the amazing ability to learn a procedural task (e.g. walking) so well that we do not need to think about the task consciously in order to accomplish it. This ability is what we call automatization. Once a task has been automatized there is no need for attention to be paid to its execution unless the expected result does not occur. At failure of expectation, deautomatization process temporarily disables the automatization effects and "conscious" control plays a role to deal with the failure situation. We implement the automatization and deautomatization cognitive functions as a self-organizing system in the IDA framework. Non-routine problem solving is the ability to devise unexpected, and often clever, solutions to problems that never been encountered before. We will present a detailed design and specification of a non-routine problem solving mechanism as a special goal context hierarchy that guides a deliberative solution search process, which we will discuss in IDA's cognitive cycle.