18 research outputs found
Sketched Answer Set Programming
Answer Set Programming (ASP) is a powerful modeling formalism for
combinatorial problems. However, writing ASP models is not trivial. We propose
a novel method, called Sketched Answer Set Programming (SkASP), aiming at
supporting the user in resolving this issue. The user writes an ASP program
while marking uncertain parts open with question marks. In addition, the user
provides a number of positive and negative examples of the desired program
behaviour. The sketched model is rewritten into another ASP program, which is
solved by traditional methods. As a result, the user obtains a functional and
reusable ASP program modelling her problem. We evaluate our approach on 21 well
known puzzles and combinatorial problems inspired by Karp's 21 NP-complete
problems and demonstrate a use-case for a database application based on ASP.Comment: 15 pages, 11 figures; to appear in ICTAI 201
Data Provenance Inference in Logic Programming: Reducing Effort of Instance-driven Debugging
Data provenance allows scientists in different domains validating their models and algorithms to find out anomalies and unexpected behaviors. In previous works, we described on-the-fly interpretation of (Python) scripts to build workflow provenance graph automatically and then infer fine-grained provenance information based on the workflow provenance graph and the availability of data. To broaden the scope of our approach and demonstrate its viability, in this paper we extend it beyond procedural languages, to be used for purely declarative languages such as logic programming under the stable model semantics. For experiments and validation, we use the Answer Set Programming solver oClingo, which makes it possible to formulate and solve stream reasoning problems in a purely declarative fashion. We demonstrate how the benefits of the provenance inference over the explicit provenance still holds in a declarative setting, and we briefly discuss the potential impact for declarative programming, in particular for instance-driven debugging of the model in declarative problem solving
Explanation Generation for Multi-Modal Multi-Agent Path Finding with Optimal Resource Utilization using Answer Set Programming
The multi-agent path finding (MAPF) problem is a combinatorial search problem
that aims at finding paths for multiple agents (e.g., robots) in an environment
(e.g., an autonomous warehouse) such that no two agents collide with each
other, and subject to some constraints on the lengths of paths. We consider a
general version of MAPF, called mMAPF, that involves multi-modal transportation
modes (e.g., due to velocity constraints) and consumption of different types of
resources (e.g., batteries). The real-world applications of mMAPF require
flexibility (e.g., solving variations of mMAPF) as well as explainability. Our
earlier studies on mMAPF have focused on the former challenge of flexibility.
In this study, we focus on the latter challenge of explainability, and
introduce a method for generating explanations for queries regarding the
feasibility and optimality of solutions, the nonexistence of solutions, and the
observations about solutions. Our method is based on answer set programming.
This paper is under consideration for acceptance in TPLP.Comment: Paper presented at the 36th International Conference on Logic
Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September
2020, 16 pages, 6 figure