TEASE-LP: Workshop on Trends, Extensions, Applications and Semantics of Logic Programming
Logic programming is a framework for expressing programs, propositions and relations as Horn clause theories, and for automatic inference in these theories. Horn clause theories are famous for its well-understood declarative semantics, in which models of logic programs are given inductively or coinductively. At the same time, Horn clauses give rise to efficient inference procedures, usually involving resolution. Logic programming found applications in type inference, verification, and AI. While logic programming was originally conceived for describing simple propositional facts, it was extended to account for much more complex theories. This includes first-order theories, higher-order theories, inductive and coinductive data, and stochastic/probabilistic theories.
The aim of this workshop is to bring together researchers that work on extensions of logic programming and inference methods, and to foster an exchange of methods and applications that have emerged in different communities.
The central idea of this workshop is to discuss the theory of logic programming and associated
topics that have as well the goal to automatically infer knowledge and proofs. Our intend is
to bring together researchers that work on the numerous topics that contribute to automatic
inference and foster an exchange that may lead to an advance in the theory of logic programming.
The topics that we have in mind are
- (Higher-order) Horn Clauses
- Relational Programming
- Horn clauses as types and type inference
- First-order and higher-order resolution
- Proof-relevant resolution
- Herbrand models and their extensions
- Least and greatest fixed point semantics of Horn clause logic
- Algebraic and coalgebraic methods for Horn clause logic
- Recursion and corecursion in Horn clause logic
- Proof theory: uniform proofs, focalised proofs, polarised lambda-calculus
- Categorical logic and semantics
- Horn clause logic for verification
- Probabilistic and inductive logic programs