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Computational Intelligence
HTN planning: complexity and expressivity
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A Computing Procedure for Quantification Theory
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Chaff: engineering an efficient SAT solver
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Autonomous Learning from the Environment
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Version Space Algebra and its Application to Programming by Demonstration
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Fast Algorithms for Mining Association Rules in Large Databases
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SATO: An Efficient Propositional Prover
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Plan evaluation with incomplete action descriptions
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ADL: An algorithmic design language for integrated circuit synthesis
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Integrating Hidden Markov Models and Spectral Analysis for Sensory Time Series Clustering
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Unifying logical and statistical AI
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Learning partially observable action schemas
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Learning partially observable action models: efficient algorithms
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Activity recognition through goal-based segmentation
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PDDL2.1: an extension to PDDL for expressing temporal planning domains
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Learning partially observable deterministic action models
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Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Searching for planning operators with context-dependent and probabilistic effects
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Domain-Driven, Actionable Knowledge Discovery
IEEE Intelligent Systems
Transferring Knowledge from Another Domain for Learning Action Models
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
Transfer Learning Action Models by Measuring the Similarity of Different Domains
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Within-problem learning for efficient lower bound computation in Max-SAT solving
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Activity recognition: linking low-level sensors to high-level intelligence
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Learning HTN method preconditions and action models from partial observations
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Learning action effects in partially observable domains
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Learning complex action models with quantifiers and logical implications
Artificial Intelligence
Incremental learning of relational action models in noisy environments
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Learning action models for multi-agent planning
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Active learning of relational action models
ILP'11 Proceedings of the 21st international conference on Inductive Logic Programming
Action-model acquisition from noisy plan traces
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Refining incomplete planning domain models through plan traces
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
A SAT-based approach to cost-sensitive temporally expressive planning
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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AI planning requires the definition of action models using a formal action and plan description language, such as the standard Planning Domain Definition Language (PDDL), as input. However, building action models from scratch is a difficult and time-consuming task, even for experts. In this paper, we develop an algorithm called ARMS (action-relation modelling system) for automatically discovering action models from a set of successful observed plans. Unlike the previous work in action-model learning, we do not assume complete knowledge of states in the middle of observed plans. In fact, our approach works when no or partial intermediate states are given. These example plans are obtained by an observation agent who does not know the logical encoding of the actions and the full state information between the actions. In a real world application, the cost is prohibitively high in labelling the training examples by manually annotating every state in a plan example from snapshots of an environment. To learn action models, ARMS gathers knowledge on the statistical distribution of frequent sets of actions in the example plans. It then builds a weighted propositional satisfiability (weighted MAX-SAT) problem and solves it using a MAX-SAT solver. We lay the theoretical foundations of the learning problem and evaluate the effectiveness of ARMS empirically.