Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An evaluation of Maes's bottom-up mechanism for behavior selection
Adaptive Behavior
Embedding robots into the Internet
Communications of the ACM
An Behavior-based Robotics
A hierarchical architecture for behavior-based robots
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
A Decision-Theoretic Approach to Planning, Perception, and Control
IEEE Expert: Intelligent Systems and Their Applications
Integrated Plan-Based Control of Autonomous Robots in Human Environments
IEEE Intelligent Systems
Applying Inexpensive AI Techniques to Computer Games
IEEE Intelligent Systems
Interaction and intelligent behavior
Interaction and intelligent behavior
Making reinforcement learning work on real robots
Making reinforcement learning work on real robots
Learning Bayesian Networks
Application of SONQL for real-time learning of robot behaviors
Robotics and Autonomous Systems
Autonomous biped gait pattern based on Fuzzy-CMAC neural networks
Integrated Computer-Aided Engineering
Modeling time-varying uncertain situations using Dynamic Influence Nets
International Journal of Approximate Reasoning
Intelligent mobile manipulator navigation using adaptive neuro-fuzzy systems
Information Sciences: an International Journal
Fuzzy temporal rules for mobile robot guidance in dynamicenvironments
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Point-based online value iteration algorithm in large POMDP
Applied Intelligence
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This paper presents a technique for a reactive mobile robot to adaptively behave in unforeseen and dynamic circumstances. A robot in nonstationary environments needs to infer how to adaptively behave to the changing environment. Behavior-based approach manages the interactions between the robot and its environment for generating behaviors, but in spite of its strengths of fast response, it has not been applied much to more complex problems for high-level behaviors. For that reason many researchers employ a behavior-based deliberative architecture. This paper proposes a 2-layer control architecture for generating adaptive behaviors to perceive and avoid moving obstacles as well as stationary obstacles. The first layer is to generate reflexive and autonomous behaviors with behavior network, and the second layer is to infer dynamic situations of the mobile robot with Bayesian network. These two levels facilitate a tight integration between high-level inference and low-level behaviors. Experimental results with various simulations and a real robot have shown that the robot reaches the goal points while avoiding stationary or moving obstacles with the proposed architecture.