Answer set programming (ASP) and conditional reasoning both are powerful and widely used methodologies from the field of knowledge representation and reasoning (KR) which are capable of formalizing default statements that usually hold but also leave room for exceptions. While ASP convinces with an intuitive rule-based syntax and fast solvers, conditionals come along with a sophisticated preference-based semantics. Here, we combine both approaches by calculating answer sets which we then prioritize based on conditional expert knowledge. We apply our hybrid approach to the task of planning warehouse layouts from the logistics domain which is predestinated for our approach because it involves, on the low-level, many variables and technical framework conditions (like rack positions) and, on the high-level, expert knowledge of the layout designer.
In this paper, we apply answer set programming (ASP) to the task of planning warehouse layouts within the logistical domain. Warehouse layout planners have to take into account a vast number of aspects to come up with feasible layouts, especially the placement of logistical elements under specific conditions like the accessibility of warehouse elements. Also optimization criteria, e. g., minimizing the pathways, have to be considered. Out of a generally large number of feasible layouts, the layout planner has to decide which layout fits best. As this can be quite difficult and time-consuming, we propose an interactive modelling environment which creates all feasible layouts for a given planning problem by exploiting a logistical knowledge base as well as specific case data provided by the user, who can interactively review all optimal layouts and look for the most suited one. It allows for taking soft constraints and vague expert knowledge into account while leaving room for proposing novel ideas for layouts.
Answer set programs used in real-world applications often require that the program is usable with different input data. This, however, can often lead to contradictory statements and consequently to an inconsistent program. Causes for potential contradictions in a program are conflicting rules. In this paper, we show how to ensure that a program
$\mathcal{P}$
remains non-contradictory given any allowed set of such input data. For that, we introduce the notion of conflict-resolving
${\lambda}$
-extensions. A conflict-resolving
${\lambda}$
-extension for a conflicting rule r is a set
${\lambda}$
of (default) literals such that extending the body of r by
${\lambda}$
resolves all conflicts of r at once. We investigate the properties that suitable
${\lambda}$
-extensions should possess and building on that, we develop a strategy to compute all such conflict-resolving
${\lambda}$
-extensions for each conflicting rule in
$\mathcal{P}$
. We show that by implementing a conflict resolution process that successively resolves conflicts using
${\lambda}$
-extensions eventually yields a program that remains non-contradictory given any allowed set of input data.
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