The focus of the 2024 Logic and Practice of Programming workshop is integrating reasoning systems for trustworthy AI, especially including integrating diverse models of programming with rules and constraints.

Trustworthy AI requires programming with rules and constraints for expressing and solving knowledge-intensive inference and combinatorial problems.  A wide range of programming models have been proposed, including but not limited to the following, and essentially all of them require or support imperative programming for use in practical applications.

1. Classical first-order logic (FOL), not supporting transitive relations, with satisfiability (SAT) and satisfiability modulo theory (SMT) solvers

2. Deductive database (primarily Datalog) systems with fact-driven inference

3. Logic programming (dominantly Prolog) systems with goal-directed search, extended with sophisticated tabling and well-founded semantics

4. Answer set programming (ASP) systems, with sophisticated grounding and solving and stable model semantics 

5. First-order logic (FOL) extended with inductive definitions (ID) 

6. Constraint logic programming(CLP) extending Prolog systems with constraints

7. Constraint programming (CP), not supporting transitive relations, with backtracking, constraint propagation, local search, and more for solving

8. Mathematical programming (MP), not supporting transitive relations, with linear programming, nonlinear programming, and much more for solving

9. Co-inductive logic programming (s(ASP), s(CASP)) extending Prolog systems with goal-directed search for ASP and co-ASP solutions

10. Advanced knowledge representation (KR) with higher-order, objects, updates, defeasible reasoning, paraconsistency, uncertainty, and probability

Given any application problem---whether for planning or scheduling or regulatory compliance, requiring logical or probabilistic reasoning, or constraint satisfaction or optimization---how to best express and solve it using one or more of the models?

In recent years, AI systems built with large neural networks trained on massive data sets (such as GPT3 with 96 layers and 175-billion parameters on 570 GB of filtered data https://arxiv.org/pdf/2005.14165)) have become increasingly capable in producing impressive outputs and beating humans in many applications. However, these systems may produce outputs that are not reliable, explainable, or aligned with intended uses.

The goal of the workshop is to bring together best people and best languages, tools, and ideas to discuss how to address these challenges with rigorous knowledge representation and reasoning, powerful and easy-to-use rule and constraint languages, and robust justifications and alignment checks. A wide variety of application problems will be used in the discussions. See here for some example application problems.

Potential participants are invited to submit a position paper (1 or 2 pages), and also to state whether they wish to present a talk at the workshop. Because we intend to bring together people from a diverse range of language and programming communities, it is essential that all talks be accessible to non-specialists.

The program committee will invite attendees based on their position paper submissions and will attempt to accommodate presentation requests, but in ways that fit with the broader organizational goals outlined above.

Please submit your position paper through this EasyChair submission URL 

https://easychair.org/conferences/?conf=lpop2024

Invited Speakers

Luc De Raedt, KU Leuven,  Belgium

Georg Gottlob, Oxford University, UK

Henry Kautz, University of Virginia, US

Chairs

Anil Nerode, Cornell University, US

Annie Liu, Stony Brook University, US

Program Committee

Martin Gebser, University of Klagenfurtm, Austria

Michael Gelfond, Texas Tech University, US

Benjamin Grosof, DARPA, US

Gopal Gupta, UT Dallas, US

Michael Kifer, Stony Brook University, US

Marta Kwiatkowska, University of Oxford, UK

Fabrizio Riguzzi, University of Ferrara, Italy

Joost Vennekens, KU Leuven, Belgium

Toby Walsh, University of New South Wales, Australia

Jan Wielemaker, CWI, The Netherlands

Roland Yap, National University of Singapore, Singapore