Building expert systems
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
The first law of robotics (a call to arms)
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Model checking
Learning Linear Constraints in Inductive Logic Programming
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Using Genetic Algorithms to Solve NP-Complete Problems
Proceedings of the 3rd International Conference on Genetic Algorithms
Learning Declarative Control Rules for Constraint-BAsed Planning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Clustering with Instance-level Constraints
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Plan evaluation with incomplete action descriptions
Eighteenth national conference on Artificial intelligence
Evolution of strategies for resource protection problems
Advances in evolutionary computing
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning hierarchical task networks by observation
ICML '06 Proceedings of the 23rd international conference on Machine learning
Cognitive architectures and general intelligent systems
AI Magazine - Special issue on achieving human-level AI through integrated systems and research
Learning Recursive Control Programs from Problem Solving
The Journal of Machine Learning Research
Semiring-Based Constraint Acquisition
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
Exploiting automatically inferred constraint-models for building identification in satellite imagery
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
K-Means with Large and Noisy Constraint Sets
ECML '07 Proceedings of the 18th European conference on Machine Learning
Structure learning of Bayesian networks using constraints
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
HTN-MAKER: learning HTNs with minimal additional knowledge engineering required
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Journal of Artificial Intelligence Research
Responsibility and blame: a structural-model approach
Journal of Artificial Intelligence Research
Macro-FF: improving AI planning with automatically learned macro-operators
Journal of Artificial Intelligence Research
Unifying SAT-based and graph-based planning
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Strategies for learning search control rules: an explanation-based approach
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
LEAP: a learning apprentice for VLSI design
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Learning about momentum conservation
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Agglomerative hierarchical clustering with constraints: theoretical and empirical results
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration
ACM Transactions on Intelligent Systems and Technology (TIST)
| Hi-index | 0.00 |
We present a Bayesian approach to learning flexible safety constraints and subsequently verifying whether plans satisfy these constraints. Our approach, called the Safety Constraint Learner/Checker (SCLC), infers safety constraints from a single expert demonstration trace and minimal background knowledge, and applies these constraints to the solutions proposed by multiple planning agents in an integrated and heterogeneous ensemble. The SCLC calculates how much to blame plan fragments (partial solutions) generated by the individual planning agents. This information is used when composing these fragments into a final overall plan. In particular, fragments whose safety violations exceed a threshold are rejected. This facilitates the generation of safe plans. We have integrated the SCLC within the Generalized Integrated Learning Architecture, which was designed for Defense Advanced Research Projects Agency (DARPA)’s Integrated Learning (IL) program. The main goal of the IL program is to promote the development and success of sophisticated systems that learn to solve challenging real-world problems based on a simple demonstration by a human expert and exiguous domain knowledge. We present experimental results showing the advantages of the SCLC on two multiagent problem-solving tasks that were benchmark applications in DARPA’s IL program. © 2012 Wiley Periodicals, Inc.