How to Build Your Own ASP-based System?!

Abstract

Answer Set Programming, or ASP for short, has become a popular and sophisticated approach to declarative problem solving. Its popularity is due to its attractive modeling-grounding-solving workflow that provides an easy approach to problem solving, even for laypersons outside computer science. However, in contrast to ASP’s ease of use, the high degree of sophistication of the underlying technology makes it even hard for ASP experts to put ideas into practice whenever this involves modifying ASP’s machinery. For addressing this issue, this tutorial aims at enabling users to build their own ASP-based systems. More precisely, we show how the ASP system clingo can be used for extending ASP and for implementing customized special-purpose systems. To this end, we propose two alternatives. We begin with a traditional AI technique and show how metaprogramming can be used for extending ASP. This is a rather light approach that relies on clingo’s reification feature to use ASP itself for expressing new functionalities. The second part of this tutorial uses traditional programming (in Python) for manipulating clingo via its application programming interface. This approach allows for changing and controlling the entire model-ground-solve workflow of ASP. Central to this is clingo’s new Application class that allows us to draw on clingo’s infrastructure by customizing processes similar to the one in clingo. For instance, we may apply manipulations to programs’ abstract syntax trees, control various forms of multi-shot solving, and set up theory propagators for foreign inferences. A cross-sectional structure, spanning meta as well as application programming, is clingo’s intermediate format, aspif, that specifies the interface among the underlying grounder and solver. We illustrate the aforementioned concepts and techniques throughout this tutorial by means of examples and several nontrivial case studies. In particular, we show how clingo can be extended by difference constraints and how guess-and-check programming can be implemented with both meta and application programming.

Multi-agent Pathfinding on Large Maps Using Graph Pruning: This Way or That Way? (Extended Abstract)

“…To test and compare the approaches to choosing the shortest paths, we performed a set of experiments on the commonly used benchmark set (Stern et al 2019). The reductionbased solver used was an ASP encoding (Gebser et al 2018) for which we used the grounding-and-solving system clingo (Kaminski et al 2020) version 5.5.2. We ran the experiments on an Intel Xeon E5-2650v4 under Debian GNU/Linux 9, with each instance limited to 300s processing time and 28 GB of memory.…”

Generative Design of the Architecture Platform in Multiprocessor System Design

“…We propose an implementation of the given design problem in ASP, a declarative programming paradigm tailored towards computationally difficult problems, especially NP-hard combinatorial search problems, and is based on the state-of-the-art API of the ASP tool clingo [35]. In the following, we give a brief overview of the core concepts that are imperative to the exploration of an application-specific architecture.…”

“…Note that a detailed description of the ASP solving process and of the ASP grounding and solving tool clingo is out of scope of this paper. An interested reader can find additional information at [35,36]. Further, a detailed explanation of the ASP encoding of the system synthesis steps, including allocation, binding, routing, and scheduling, can be found at [10].…”

Evolutionary System Design with Answer Set Programming

“…This section describes how we implement Pareto optimization. To describe preferences, we use the fact format of the system asprin described in [13], and to achieve grounding, solving, and optimization, we employ a Python script based on clingo's theory and application framework [14]; the script is a re-implementation of the design space exploration system described in [2]. The general idea is that difference constraints as well as Pareto optimization are handled by background theories that are integrated via clingo's theory interface.…”