SAT-Based Cooperative Planning: A Proposal

@inproceedings{Benedetti2005SATBasedCP,
  title={SAT-Based Cooperative Planning: A Proposal},
  author={Marco Benedetti and Luigia Carlucci Aiello},
  booktitle={Mechanizing Mathematical Reasoning},
  year={2005}
}
We present a work-in-progress on distributed planning, which relies on the “planning as satisfiability” paradigm. It allows for multi-agent cooperative planning by joining SAT-based planning and a particular approach to distributed propositional satisfiability. Each agent is thus enabled to plan on its own and communicate with other agents during the planning process, in such a way that synchronized and possibly cooperative plans come out as a result. We discuss in some details both piers of… 
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References

SHOWING 1-10 OF 27 REFERENCES
A Survey of Research in Distributed, Continual Planning
TLDR
It is argued that developing DCP systems will be necessary for planning applications to be successful in these environments and a historical overview of research leading to the current state of the art in DCP is given.
Pushing the Envelope: Planning, Propositional Logic and Stochastic Search
TLDR
Stochastic methods are shown to be very effective on a wide range of scheduling problems, but this is the first demonstration of its power on truly challenging classical planning instances.
Planning as Satisfiability
SATPLAN04 is a updated version of the planning as satisfiability approach originally proposed in (Kautz & Selman 1992; 1996) using hand-generated translations, and implemented for PDDL input in the
Automatic SAT-Compilation of Planning Problems
TLDR
A fully-implemented compiler is described that can generate eight major encodings and a number of subsidiary ones, and the compiler is tested on a suite of STRIPS planning problems in order to determine whichencodings have the best properties.
Reasoning about reasoning in a meta-level architecture
TLDR
The paper illustrates the meta-level architecture the author proposes for problem solving in a multi-agent scenario, and discusses the approach in relation to the modal one and compares it with other meta- level architectures based on logic.
PSATO: a Distributed Propositional Prover and its Application to Quasigroup Problems
TLDR
A distributed/parallel prover for propositional satisfiability (SAT), called PSATO, for networks of workstations based on the sequential SAT prover SATO, which is an efficient implementation of the Davis –Putnam algorithm.
Parallel cooperative propositional theorem proving
  • Fumiaki Okushi
  • Computer Science
    Annals of Mathematics and Artificial Intelligence
  • 2004
TLDR
The paper presents an algorithm to combine two arbitrary autarkies to form a larger autarky, which achieves speedup greater than the number of processors for many of the formulas.
Foundations of distributed artificial intelligence
TLDR
This paper presents a meta-modelling architecture for distributed artificial intelligence that automates the very labor-intensive and therefore time-heavy and expensive process of designing and implementing distributed systems.
A taxonomy of parallel strategies for deduction
  • M. Bonacina
  • Computer Science
    Annals of Mathematics and Artificial Intelligence
  • 2004
TLDR
This paper presents a taxonomy of parallel theorem-proving methods based on the control of search, the granularity of parallelism and the nature of the method, and analyzes how the different approaches to parallelization affect theControl of search.
Computers and Intractability: A Guide to the Theory of NP-Completeness
Horn formulae play a prominent role in artificial intelligence and logic programming. In this paper we investigate the problem of optimal compression of propositional Horn production rule knowledge
...
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