Explainable Planning Using Answer Set Programming

2020 ◽  
Author(s):  
Van Nguyen ◽  
Stylianos Loukas Vasileiou ◽  
Tran Cao Son ◽  
William Yeoh
Keyword(s):  
Optimization Problem ◽  
Answer Set Programming ◽  
Popular Approach ◽  
Planning Problems ◽  
Do So ◽  
Answer Set

In human-aware planning problems, the planning agent may need to explain its plan to a human user, especially when the plan appears infeasible or suboptimal for the user. A popular approach to do so is called model reconciliation, where the planning agent tries to reconcile the differences between its model and the model of the user such that its plan is also feasible and optimal to the user. This problem can be viewed as an optimization problem, where the goal is to find a subset-minimal explanation that one can use to modify the model of the user such that the plan of the agent is also feasible and optimal to the user. This paper presents an algorithm for solving such problems using answer set programming.

scholarly journals Answer Set Planning Under Action Costs

2003 ◽  
Vol 19 ◽  
pp. 25-71 ◽  
Author(s):  
T. Eiter ◽  
W. Faber ◽  
N. Leone ◽  
G. Pfeifer ◽  
A. Polleres
Keyword(s):  
Answer Set Programming ◽  
Optimality Criteria ◽  
Experimental Results ◽  
Logic Programs ◽  
Planning Systems ◽  
Planning Problems ◽  
Answer Set

Recently, planning based on answer set programming has been proposed as an approach towards realizing declarative planning systems. In this paper, we present the language Kc, which extends the declarative planning language K by action costs. Kc provides the notion of admissible and optimal plans, which are plans whose overall action costs are within a given limit resp. minimum over all plans (i.e., cheapest plans). As we demonstrate, this novel language allows for expressing some nontrivial planning tasks in a declarative way. Furthermore, it can be utilized for representing planning problems under other optimality criteria, such as computing ``shortest'' plans (with the least number of steps), and refinement combinations of cheapest and fastest plans. We study complexity aspects of the language Kc and provide a transformation to logic programs, such that planning problems are solved via answer set programming. Furthermore, we report experimental results on selected problems. Our experience is encouraging that answer set planning may be a valuable approach to expressive planning systems in which intricate planning problems can be naturally specified and solved.

scholarly journals Planning with preferences using logic programming

2006 ◽  
Vol 6 (5) ◽  
pp. 559-607 ◽  
Author(s):  
TRAN CAO SON ◽  
ENRICO PONTELLI
Keyword(s):  
Logic Programming ◽  
Answer Set Programming ◽  
Planning Problem ◽  
Declarative Language ◽  
High Level ◽  
Planning Problems ◽  
Answer Set

We present a declarative language, ${\cal PP}$, for the high-level specification of preferences between possible solutions (or trajectories) of a planning problem. This novel language allows users to elegantly express non-trivial, multi-dimensional preferences and priorities over such preferences. The semantics of ${\cal PP}$ allows the identification of most preferred trajectories for a given goal. We also provide an answer set programming implementation of planning problems with ${\cal PP}$ preferences.

scholarly journals Optimizing phylogenetic supertrees using answer set programming

2015 ◽  
Vol 15 (4-5) ◽  
pp. 604-619 ◽  
Author(s):  
LAURA KOPONEN ◽  
EMILIA OIKARINEN ◽  
TOMI JANHUNEN ◽  
LAURA SÄILÄ
Keyword(s):  
Phylogenetic Trees ◽  
Optimization Problem ◽  
Matrix Representation ◽  
Answer Set Programming ◽  
Heuristic Methods ◽  
Construction Problem ◽  
Single Tree ◽  
Conflicting Information ◽  
The Family ◽  
Answer Set

AbstractThe supertree construction problem is about combining several phylogenetic trees with possibly conflicting information into a single tree that has all the leaves of the source trees as its leaves and the relationships between the leaves are as consistent with the source trees as possible. This leads to an optimization problem that is computationally challenging and typically heuristic methods, such as matrix representation with parsimony (MRP), are used. In this paper we consider the use of answer set programming to solve the supertree construction problem in terms of two alternative encodings. The first is based on an existing encoding of trees using substructures known as quartets, while the other novel encoding captures the relationships present in trees through direct projections. We use these encodings to compute a genus-level supertree for the family of cats (Felidae). Furthermore, we compare our results to recent supertrees obtained by the MRP method.

scholarly journals Planning with Incomplete Information in Quantified Answer Set Programming

2021 ◽  
pp. 1-17
Author(s):  
JORGE FANDINNO ◽  
FRANCOIS LAFERRIERE ◽  
JAVIER ROMERO ◽  
TORSTEN SCHAUB ◽  
TRAN CAO SON
Keyword(s):  
Incomplete Information ◽  
Answer Set Programming ◽  
Transition Function ◽  
Logic Programs ◽  
Quantified Boolean Formulas ◽  
Boolean Formulas ◽  
Simple Formalism ◽  
Planning Problems ◽  
Answer Set ◽  
Conditional Planning

Abstract We present a general approach to planning with incomplete information in Answer Set Programming (ASP). More precisely, we consider the problems of conformant and conditional planning with sensing actions and assumptions. We represent planning problems using a simple formalism where logic programs describe the transition function between states, the initial states and the goal states. For solving planning problems, we use Quantified Answer Set Programming (QASP), an extension of ASP with existential and universal quantifiers over atoms that is analogous to Quantified Boolean Formulas (QBFs). We define the language of quantified logic programs and use it to represent the solutions different variants of conformant and conditional planning. On the practical side, we present a translation-based QASP solver that converts quantified logic programs into QBFs and then executes a QBF solver, and we evaluate experimentally the approach on conformant and conditional planning benchmarks.

scholarly journals Open answer set programming with guarded programs

2008 ◽  
Vol 9 (4) ◽  
pp. 1-53 ◽  
Author(s):  
Stijn Heymans ◽  
Davy Van Nieuwenborgh ◽  
Dirk Vermeir
Keyword(s):  
Answer Set Programming ◽  
Answer Set

scholarly journals Exhaustively characterizing feasible logic models of a signaling network using Answer Set Programming

2013 ◽  
Vol 29 (18) ◽  
pp. 2320-2326 ◽  
Author(s):  
Carito Guziolowski ◽  
Santiago Videla ◽  
Federica Eduati ◽  
Sven Thiele ◽  
Thomas Cokelaer ◽  
...  
Keyword(s):  
Answer Set Programming ◽  
Signaling Network ◽  
Logic Models ◽  
Answer Set

CoreALMlib: An library translated from the Component Library

2016 ◽  
Vol 16 (5-6) ◽  
pp. 800-816 ◽  
Author(s):  
DANIELA INCLEZAN
Keyword(s):  
Natural Language ◽  
Answer Set Programming ◽  
Natural Language Understanding ◽  
Inference Engine ◽  
Lexical Resources ◽  
Language Understanding ◽  
Commonsense Knowledge ◽  
Component Library ◽  
Action Language ◽  
Answer Set

AbstractThis paper presents CoreALMlib, an $\mathscr{ALM}$ library of commonsense knowledge about dynamic domains. The library was obtained by translating part of the Component Library (CLib) into the modular action language $\mathscr{ALM}$. CLib consists of general reusable and composable commonsense concepts, selected based on a thorough study of ontological and lexical resources. Our translation targets CLibstates (i.e., fluents) and actions. The resulting $\mathscr{ALM}$ library contains the descriptions of 123 action classes grouped into 43 reusable modules that are organized into a hierarchy. It is made available online and of interest to researchers in the action language, answer-set programming, and natural language understanding communities. We believe that our translation has two main advantages over its CLib counterpart: (i) it specifies axioms about actions in a more elaboration tolerant and readable way, and (ii) it can be seamlessly integrated with ASP reasoning algorithms (e.g., for planning and postdiction). In contrast, axioms are described in CLib using STRIPS-like operators, and CLib's inference engine cannot handle planning nor postdiction.

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