The society of mind
C4.5: programs for machine learning
C4.5: programs for machine learning
Identifying qualitatively different outcomes of actions: gaining autonomy through learning
AGENTS '00 Proceedings of the fourth international conference on Autonomous agents
Systems That Know What They're Doing
IEEE Intelligent Systems
RPLLEARN: Extending an Autonomous Robot Control Language to Perform
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Motion and Episode Models for (Simulated) Football Games: Acquisition, Representation, and Use
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Computerized Real-Time Analysis of Football Games
IEEE Pervasive Computing
Action awareness: enabling agents to optimize, transform, and coordinate plans
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
The 3rd international planning competition: results and analysis
Journal of Artificial Intelligence Research
Optimized execution of action chains using learned performance models of abstract actions
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Cognitive Technical Systems -- What Is the Role of Artificial Intelligence?
KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
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This paper proposes GrAM (Grounded Action Models), a novel integration of actions and action models into the knowledge representation and inference mechanisms of agents. In GrAM action models accord to agent behavior and can be specified explicitly and implicitly. The explicit representation is an action class specific set of Markov logic rules that predict action properties. Stated implicitly an action model defines a data mining problem that, when executed, computes the model's explicit representation. When inferred from an implicit representation the prediction rules predict typical behavior and are learned from a set of training examples, or, in other words, grounded in the respective experience of the agents. Therefore, GrAM allows for the functional and thus adaptive specification of concepts such as the class of situations in which a special action is typically executed successfully or the concept of agents that tend to execute certain kinds of actions. GrAM represents actions and their models using an upgrading of the representation language OWL and equips the Java Theorem Prover (JTP), a hybrid reasoner for OWL, with additional mechanisms that allow for the automatic acquisition of action models and solving a variety of inference tasks for actions, action models and functional descriptions.