Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Zero-suppressed BDDs for set manipulation in combinatorial problems
DAC '93 Proceedings of the 30th international Design Automation Conference
STRIPS: a new approach to the application of theorem proving to problem solving
Computation & intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
PROLOG Programming for Artificial Intelligence
PROLOG Programming for Artificial Intelligence
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Human-Level AI's Killer Application: Interactive Computer Games
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Machine Learning
Learning planning rules in noisy stochastic worlds
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
ProbLog: a probabilistic prolog and its application in link discovery
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
A simple-transition model for relational sequences
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Probabilistic inductive logic programming
Probabilistic inductive logic programming
Representing causal information about a probabilistic process
JELIA'06 Proceedings of the 10th European conference on Logics in Artificial Intelligence
Trading expressivity for efficiency in statistical relational learning: Ph.D. thesis abstract
ACM SIGKDD Explorations Newsletter
Don't fear optimality: sampling for probabilistic-logic sequence models
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Learning the parameters of probabilistic logic programs from interpretations
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
ciForager: Incrementally discovering regions of correlated change in evolving graphs
ACM Transactions on Knowledge Discovery from Data (TKDD)
Expectation maximization over binary decision diagrams for probabilistic logic programs
Intelligent Data Analysis
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Artificial intelligence aims at developing agents that learn and act in complex environments. Realistic environments typically feature a variable number of objects, relations amongst them, and non-deterministic transition behavior. Standard probabilistic sequence models provide efficient inference and learning techniques, but typically cannot fully capture the relational complexity. On the other hand, statistical relational learning techniques are often too inefficient. In this paper, we present a simple model that occupies an intermediate position in this expressiveness/efficiency trade-off. It is based on CP-logic, an expressive probabilistic logic for modeling causality. However, by specializing CP-logic to represent a probability distribution over sequences of relational state descriptions, and employing a Markov assumption, inference and learning become more tractable and effective. We show that the resulting model is able to handle probabilistic relational domains with a substantial number of objects and relations.