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A review of machine learning for automated planning
Recent discoveries in automated planning are broadening the scope of planners, from toy
problems to real applications. However, applying automated planners to real-world …
problems to real applications. However, applying automated planners to real-world …
Statistical relational artificial intelligence: Logic, probability, and computation
An intelligent agent interacting with the real world will encounter individual people, courses,
test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …
test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …
[کتاب][B] Introduction to statistical relational learning
Advanced statistical modeling and knowledge representation techniques for a newly
emerging area of machine learning and probabilistic reasoning; includes introductory …
emerging area of machine learning and probabilistic reasoning; includes introductory …
Graph Learning for Numeric Planning
Graph learning is naturally well suited for use in symbolic, object-centric planning due to its
ability to exploit relational structures exhibited in planning domains and to take as input …
ability to exploit relational structures exhibited in planning domains and to take as input …
Asnets: Deep learning for generalised planning
In this paper, we discuss the learning of generalised policies for probabilistic and classical
planning problems using Action Schema Networks (ASNets). The ASNet is a neural network …
planning problems using Action Schema Networks (ASNets). The ASNet is a neural network …
Approximate policy iteration with a policy language bias
We explore approximate policy iteration, replacing the usual costfunction learning step with
a learning step in policy space. We give policy-language biases that enable solution of very …
a learning step in policy space. We give policy-language biases that enable solution of very …
Reinforcement learning for classical planning: Viewing heuristics as dense reward generators
Recent advances in reinforcement learning (RL) have led to a growing interest in applying
RL to classical planning domains or applying classical planning methods to some complex …
RL to classical planning domains or applying classical planning methods to some complex …
Practical solution techniques for first-order MDPs
Many traditional solution approaches to relationally specified decision-theoretic planning
problems (eg, those stated in the probabilistic planning domain description language, or …
problems (eg, those stated in the probabilistic planning domain description language, or …
First order decision diagrams for relational MDPs
Markov decision processes capture sequential decision making under uncertainty, where an
agent must choose actions so as to optimize long term reward. The paper studies efficient …
agent must choose actions so as to optimize long term reward. The paper studies efficient …
Deep learning for generalised planning with background knowledge
Automated planning is a form of declarative problem solving which has recently drawn
attention from the machine learning (ML) community. ML has been applied to planning …
attention from the machine learning (ML) community. ML has been applied to planning …