Implementing Preferences with asprin

Self Cite

223

198

We introduce a new flexible paradigm of grounding and solving in Answer Set Programming (ASP), which we refer to as multi-shot ASP solving, and present its implementation in the ASP systemclingo. Multi-shot ASP solving features grounding and solving processes that deal with continuously changing logic programs. In doing so, they remain operative and accommodate changes in a seamless way. For instance, such processes allow for advanced forms of search, as in optimization or theory solving, or interaction with an environment, as in robotics or query answering. Common to them is that the problem specification evolves during the reasoning process, either because data or constraints are added, deleted, or replaced. This evolutionary aspect adds another dimension to ASP since it brings about state changing operations. We address this issue by providing an operational semantics that characterizes grounding and solving processes in multi-shot ASP solving. This characterization provides a semantic account of grounder and solver states along with the operations manipulating them. The operative nature of multi-shot solving avoids redundancies in relaunching grounder and solver programs and benefits from the solver's learning capacities.clingoaccomplishes this by complementing ASP's declarative input language with control capacities. On the declarative side, a new directive allows for structuring logic programs into named and parameterizable subprograms. The grounding and integration of these subprograms into the solving process is completely modular and fully controllable from the procedural side. To this end,clingooffers a new application programming interface that is conveniently accessible via scripting languages. By strictly separating logic and control,clingoalso abolishes the need for dedicated systems for incremental and reactive reasoning, likeiclingoandoclingo, respectively, and its flexibility goes well beyond the advanced yet still rigid solving processes of the latter.

Section: Resultsmentioning

confidence: 99%

“…This provides an easy way of applying program transformations on the non-ground level without any burden of parsing input programs in their full generality. This feature is exploited by the systems asprin (Brewka et al 2015b) for expressing preferences and anthem 36 for formula extraction.…”

Section: Application Program Interfacesmentioning

confidence: 99%

Multi-shot ASP solving with clingo

Gebser

Kaminski

Kaufmann

et al. 2018

Self Cite

223

198

“…Finally, subset minimality is among the preferences natively supported in the language of asprin (Brewka et al 2015a(Brewka et al , 2015bRomero et al 2016), a versatile framework built on top of clingo (Gebser et al 2017). The algorithm implemented by asprin is also iterative, meaning that better and better models are computed until an inconsistency arises.…”

Section: Related Workmentioning

confidence: 99%

Model enumeration in propositional circumscription via unsatisfiable core analysis

Alviano¹

2017

Many practical problems are characterized by a preference relation over admissible solutions, where preferred solutions are minimal in some sense. For example, a preferred diagnosis usually comprises a minimal set of reasons that is sufficient to cause the observed anomaly. Alternatively, a minimal correction subset comprises a minimal set of reasons whose deletion is sufficient to eliminate the observed anomaly. Circumscription formalizes such preference relations by associating propositional theories with minimal models. The resulting enumeration problem is addressed here by means of a new algorithm taking advantage of unsatisfiable core analysis. Empirical evidence of the efficiency of the algorithm is given by comparing the performance of the resulting solver, CIRCUMSCRIPTINO, with HCLASP, CAMUS MCS, LBX and MCSLS on the enumeration of minimal models for problems originating from practical applications.

“…In this way, we use clingo as a black box and implement all examples by consecutive clingo calls. Meta programming is for example used in asprin (Brewka et al . 2015) and plasp (Dimopoulos et al .…”

Section: Introductionmentioning

confidence: 99%

How to Build Your Own ASP-based System?!

Kaminski¹,

Romero²,

Schaub³

et al. 2021

Self Cite

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.