An Agent Based Framework for Adaptive Control and Decision Making of Autonomous Vehicles

The paper addresses the problem of defining a theoretical physical agent framework that combines rational agent decision making with abstractions from predictions and planning of the future of the physical environment. The objective of the new framework is to reduce complexity of logical inference of agents controlling autonomous vehicles and robots in space exploration, deep underwater exploration, defense reconnaissance, automated manufacturing and household automation. An essential feature of the framework is automated realtime evaluations of abstractions on the effects of future actions. Comparison is made with hybrid automaton based solutions in terms of computational complexity.