CP 2018 Workshop on Constraints and AI Planning constraints-aip2018

Call for paper 〔OPEN〕

〔CLOSED〕

Registration 〔OPEN〕

〔CLOSED〕

Introduction

For this first one-day workshop on the topic of Constraints and AI Planning we take a broad view stemming from the observation that there are fundamental differences in representing and reasoning about a problem via constraints, as is common in CP and optimization, and doing so in the form of a state-transition system, as is common in AI planning, heuristic search, and dynamic programming. Therefore we hope to attract presentations and discussions on the interactions, overlaps, and differences among CP, SAT, mixed integer programming, AI planning in its many forms (e.g., classical, numeric, temporal, stochastic), heuristic search (i.e., A*-style state-based search) and dynamic programming. We are particularly interested in combinations of two or more perspectives. This workshop should interest researchers from academia and industry in the areas of constraint programming, operations research, and AI planning.

Committee

Christopher Beck, University of Toronto, jcb@mie.utoronto.ca
Michael Cashmore, King's College London, michael.cashmore@kcl.ac.uk
Malte Helmert, University of Basel, malte.helmert@unibas.ch
Gilles Pesant, Polytechnique Montreal, gilles.pesant@polymtl.ca

Call for paper

Important date

2018-07-06

Abstract submission deadline

2018-07-06

Draft paper submission deadline

2018-07-10

Draft paper acceptance notification

Main areas of interest include, but are not restricted to:

Using constraint-based techniques (e.g., CP, SAT, SMT, MIP) for solving AI planning problems including contributing to components of the AI planning solution such as heuristic evaluation.
Theoretical/formal work comparing constraint-based and state-based representation and reasoning.
Empirical studies comparing constraint-based and state-based solution approaches on common classes of problems.
Extending approaches primarily developed in one area to the other (e.g., Lagrangian relaxation, Logic-based Benders decomposition, A*-style search, abstraction).
Understanding and comparing techniques that have been developed and applied independently in both optimization/OR and AI literatures (e.g. multi-valued decision diagrams, dynamic programming vs. heuristic search, Markov Decision Processes).