Cooperative multi-agent planning (MAP) is a relatively recent research field that combines
technologies, algorithms, and techniques developed by the Artificial Intelligence Planning …

Answer Set Planning refers to the use of Answer Set Programming (ASP) to compute plans,
that is, solutions to planning problems, that transform a given state of the world to another …

LAMA is a classical planning system based on heuristic forward search. Its core feature is
the use of a pseudo-heuristic derived from landmarks, propositional formulas that must be …

Advanced statistical modeling and knowledge representation techniques for a newly
emerging area of machine learning and probabilistic reasoning; includes introductory …

Delineating the tremendous growth in this area, the Handbook of Approximation Algorithms
and Metaheuristics covers fundamental, theoretical topics as well as advanced, practical …

Landmarks for propositional planning tasks are variable assignments that must occur at
some point in every solution plan. We propose a novel approach for using landmarks in …

Opponent modeling is the ability to use prior knowledge and observations in order to predict
the behavior of an opponent. This survey presents a comprehensive overview of existing …

It has been shown recently that the performance of greedy best-first search (GBFS) for
computing plans that are not necessarily optimal can be improved by adding forms of …

We introduce a width parameter that bounds the complexity of classical planning problems
and domains, along with a simple but effective blind-search procedure that runs in time that …

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 …