Linear Logic Programming for Narrative Generation

Abstract. In this paper, we explore the use of Linear Logic programming for story generation. We use the language Celf to represent narrative knowledge, and its own querying mechanism to generate story instances, through a number of proof terms. Each proof term obtained is used, through a resource-flow analysis, to build a directed graph where nodes are narrative actions and edges represent inferred causality relationships. Such graphs represent narrative plots structured by narrative causality. Building on previous work evidencing the suitability of Linear Logic as a conceptual model of action and change for narratives, we explore the conditions under which these representations can be operationalized through Linear Logic Programming techniques. This approach is a candidate technique for narrative generation which unifies declarative representations and generation via query and deduction mechanisms

Deductive synthesis of recursive plans in linear logic

Linear logic has previously been shown to be suitable for describing and deductively solving planning problems involving conjunction and disjunction. We introduce a recursively defined datatype and a corresponding induction rule, thereby allowing recursive plans to be synthesised. In order to make explicit the relationship between proofs and plans, we enhance the linear logic deduction rules to handle plans as a form of proof term

Focusing and Proof-Nets in Linear and Non-Commutative Logic

Linear Logic [4] has raised a lot of interest in computer research, especially because of its resource sensitive nature. One line of research studies proof construction procedures and their interpretation as computational models, in the "Logic Programming" tradition. An efficient proof search procedure, based on a proof normalization result called "Focusing", has been described in [2]. Focusing is described in terms of the sequent system of commutative Linear Logic, which it refines in two steps. It is shown here that Focusing can also be interpreted in the proof-net formalism, where it appears, at least in the multiplicative fragment, to be a simple refinement of the "Splitting lemma" for proof-nets. This change of perspective allows to generalize the Focusing result to (the multiplicative fragment of) any logic where the "Splitting lemma" holds. This is, in particular, the case of the Non-Commutative logic of [1], and all the computational exploitation of Focusing which has been performed in the commutative case can thus be revised and adapted to the non commutative case

A logical characterization of forward and backward chaining in the inverse method

Abstract. The inverse method is a generalization of resolution that can be applied to non-classical logics. We have recently shown how Andreoli’s focusing strategy can be adapted for the inverse method in linear logic. In this paper we introduce the notion of focusing bias for atoms and show that it gives rise to forward and backward chaining, generalizing both hyperresolution (forward) and SLD resolution (backward) on the Horn fragment. A key feature of our characterization is the structural, rather than purely operational, explanation for forward and backward chaining. A search procedure like the inverse method is thus able to perform both operations as appropriate, even simultaneously. We also present experimental results and an evaluation of the practical benefits of biased atoms for a number of examples from different problem domains.

A focusing inverse method theorem prover for first-order linear logic

Abstract. We present the theory and implementation of a theorem prover forfirst-order intuitionistic linear logic based on the inverse method. The central proof-theoretic insights underlying the prover concern resource management andfocused derivations, both of which are traditionally understood in the domain of backward reasoning systems such as logic programming. We illustrate how re-source management, focusing, and other intrinsic properties of linear connectives affect the basic forward operations of rule application, contraction, and forwardsubsumption. We also present some preliminary experimental results obtained with our implementation