Generating plans in linear logic

Masseron, M.; Tollu, Christophe; Vauzeilles, Jacqueline

doi:10.1007/3-540-53487-3_35

Cited by 36 publications

(28 citation statements)

References 6 publications

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“…RAPS showed that the research in this area can be helpful and can provide an improvement for planners. This paper extends the previous research in this area [5,22] by providing detailed description of encoding of planning problems into Linear Logic. In addition, we provide an encoding of many optimizations of planning problems, which helps a planner to find a solution more quickly.…”

Section: Introductionmentioning

confidence: 65%

“…Idea of a reduction of the problem of plan generation to finding a proof in Linear Logic was previously studied at [5,15,22]. At first we focus on such planning problems whose actions do not contain negative preconditions.…”

Section: Basic Encoding Of Planning Problemsmentioning

confidence: 99%

“…Previous research showed that planning problems can be simply encoded in Linear Logic and a problem of plan generation can be reduced to a problem of finding a proof in linear logic. First ideas of solving planning problems via theorem proving in Linear Logic has been shown at [3,15,22]. These papers showed that M?LL fragment of Linear Logic is strong enough to formalize planning problems.…”

Section: Introductionmentioning

confidence: 99%

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Encoding of Planning Problems and Their Optimizations in Linear Logic

Chrpa

Surynek

Vyskočil

2009

Lecture Notes in Computer Science

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Abstract. Girard's Linear Logic is a formalism which can be used to manage a lot of problems with consumable resources. Its expressiveness is quite good for an easily understandable encoding of many problems. We concentrated on expressing planning problems by linear logic in this paper. We observed a rich usage of a construct of consumable resources in planning problem formulations. This fact motivates us to provide a possible encoding of planning problems in linear logic. This paper shows how planning problems can be encoded in Linear Logic and how some optimizations of planning problems can be encoded. These optimizations can help planners to improve the efficiency of finding solutions (plans).

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Section: Introductionmentioning

confidence: 65%

Section: Basic Encoding Of Planning Problemsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Encoding of Planning Problems and Their Optimizations in Linear Logic

Chrpa

Surynek

Vyskočil

2009

Lecture Notes in Computer Science

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“…Another planning logic that is very close to ours is the one developed by Masseron et al [20]. One important difference is the fact that in their formalism it is possible to include nondeterministic actions, i.e., actions with an outcome that is uncertain albeit limited to a set of given outcomes.…”

Section: Relation With Other Planning Logicsmentioning

confidence: 99%

Untitled

Weerdt

Bos

Tonino

et al. 2003

Annals of Mathematics and Artificial Intelligence

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In a multi-agent system, agents are carrying out certain tasks by executing plans. Consequently, the problem of finding a plan, given a certain goal, has been given a lot of attention in the literature. Instead of concentrating on this problem, the focus of this paper is on cooperation between agents which already have constructed plans for their goals. By cooperating, agents might reduce the number of actions they have to perform in order to fulfill their goals. The key idea is that in carrying out a plan an agent possibly produces side products that can be used as resources by other agents. As a result, an other agent can discard some of its planned actions. This process of exchanging products, called plan merging, results in distributed plans in which agents become dependent on each other, but are able to attain their goals more efficiently. In order to model this kind of cooperation, a new formalism is developed in which side products are modeled explicitly. The formalism is a resource logic based on the notions of resource, skill, goal, and service. Starting with some resources, an agent can perform a number of skills in order to produce other resources which suffice to achieve some given goals. Here, a skill is an elementary production process taking as inputs resources satisfying certain constraints. A service is a serial or parallel composition of skills acting as a program. An operational semantics is developed for these services as programs. Using this formalism, an algorithm for plan merging is developed, which is anytime and runs in polynomial time. Furthermore, a variant of this algorithm is proposed that handles the exchange of resources in a more flexible way. The ideas in the paper will be illustrated by an example from public transportation.

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“…No frame axioms about the environment [18] need to be stated and one only has to deal with axioms about those objects which are involved in the action. Proof search in linear logic will therefore have many useful applications such as resource sensitive logic programming [14], modeling concurrent computation by petri nets [11], and planning [17].…”

Section: Introductionmentioning

confidence: 99%

Connection-based proof construction in linear logic

Kreitz

Mantel

Otten

et al. 1997

Automated Deduction—CADE-14

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Abstract. We present a matrix characterization of logical validity in the multiplicative fragment of linear logic. On this basis we develop a matrix-based proof search procedure for this fragment and a procedure which translates the machine-found proofs back into the usual sequent calculus for linear logic. Both procedures are straightforward extensions of methods which originally were developed for a uniform treatment of classical, intuitionistic and modal logics. They can be extended to further fragments of linear logic once a matrix characterization has been found.

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Generating plans in linear logic

Cited by 36 publications

References 6 publications

Encoding of Planning Problems and Their Optimizations in Linear Logic

Encoding of Planning Problems and Their Optimizations in Linear Logic

Untitled

Connection-based proof construction in linear logic

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