Reasoning about action I: a possible worlds approach
Artificial Intelligence
Combining logic and differential equations for describing real-world systems
Proceedings of the first international conference on Principles of knowledge representation and reasoning
High-level planning and low-level execution: towards a complete robotic agent
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Intelligent planning: a decomposition and abstraction based approach
Intelligent planning: a decomposition and abstraction based approach
ConGolog, a concurrent programming language based on the situation calculus
Artificial Intelligence
Robust Monte Carlo localization for mobile robots
Artificial Intelligence
The Qualification Problem: A solution to the problem of anomalous models
Artificial Intelligence
Adding Priorities and Specificity to Default Logic
JELIA '94 Proceedings of the European Workshop on Logics in Artificial Intelligence
GOLEX - Bridging the Gap between Logic (GOLOG) and a Real Robot
KI '98 Proceedings of the 22nd Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Addressing the Qualification Problem in FLUX
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
Knowledge, action, and the frame problem
Artificial Intelligence
FLUX: A logic programming method for reasoning agents
Theory and Practice of Logic Programming
The concurrent, continuous FLUX
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The focussed D* algorithm for real-time replanning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
FLUX: A logic programming method for reasoning agents
Theory and Practice of Logic Programming
A Logic-Based Approach to Finding Explanations for Discrepancies in Optimistic Plan Execution
Fundamenta Informaticae
Monitoring the execution of robot plans using semantic knowledge
Robotics and Autonomous Systems
On reversing actions: algorithms and complexity
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A temporal logic-based planning and execution monitoring framework for unmanned aircraft systems
Autonomous Agents and Multi-Agent Systems
Detecting and repairing anomalous evolutions in noisy environments
Annals of Mathematics and Artificial Intelligence
KMONITOR: a tool for monitoring plan execution in action theories
LPNMR'05 Proceedings of the 8th international conference on Logic Programming and Nonmonotonic Reasoning
Acquiring observation models through reverse plan monitoring
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
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We present a robot control system for known structured environments that integrates robust reactive control with reasoning-based execution monitoring. It provides a robot with a powerful method for dealing with situations that were caused by the interaction with humans or that are due to unexpected changes in the operating environment. On the reactive level, the robot is controlled using a hierarchy of low-level behaviours. On the high level, a logical representation of the world enables the robot to plan action sequences and to reason about the state of the world. If the execution of an action does not have the expected effect, high-level reasoning allows the robot to infer possible explanations and, if necessary, to recover from the failure situation. For the robot to act optimally, the discrepancies between the internal world model and the real world have to be detected and corrected. The proposed system obtains new information about the world by executing sensing actions (active perception) and by sensory interpretation during the robot's operation. It also takes into account temporal information about changes in the environment. All updates of the world model are performed in a way that the changes are consistent with an underlying action theory. Having implemented the proposed system on a common mobile robot platform, we demonstrate the value of intelligent execution monitoring by means of two realistic office delivery scenarios.