scholarly journals A Trajectory Calculus for Qualitative Spatial Reasoning Using Answer Set Programming

2018 ◽  
Vol 18 (3-4) ◽  
pp. 355-371 ◽  
Author(s):  
GEORGE BARYANNIS ◽  
ILIAS TACHMAZIDIS ◽  
SOTIRIS BATSAKIS ◽  
GRIGORIS ANTONIOU ◽  
MARIO ALVIANO ◽  
...  
Keyword(s):  
Natural Language ◽  
Real World ◽  
Moving Objects ◽  
Spatial Reasoning ◽  
Spatial Information ◽  
Scale Up ◽  
Answer Set Programming ◽  
Qualitative Spatial Reasoning ◽  
Practical Applications ◽  
Answer Set

AbstractSpatial information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as bus routes planning. Representing and reasoning on trajectories is a specific case of qualitative spatial reasoning that focuses on moving objects and their paths. In this work, we propose two versions of a trajectory calculus based on the allowed properties over trajectories, where trajectories are defined as a sequence of non-overlapping regions of a partitioned map. More specifically, if a given trajectory is allowed to start and finish at the same region, 6 base relations are defined (TC-6). If a given trajectory should have different start and finish regions but cycles are allowed within, 10 base relations are defined (TC-10). Both versions of the calculus are implemented as ASP programs; we propose several different encodings, including a generalised program capable of encoding any qualitative calculus in ASP. All proposed encodings are experimentally evaluated using a real-world dataset. Experiment results show that the best performing implementation can scale up to an input of 250 trajectories for TC-6 and 150 trajectories for TC-10 for the problem of discovering a consistent configuration, a significant improvement compared to previous ASP implementations for similar qualitative spatial and temporal calculi.

scholarly journals Generalized Target Assignment and Path Finding Using Answer Set Programming

2017 ◽  
Author(s):  
Van Nguyen ◽  
Philipp Obermeier ◽  
Tran Cao Son ◽  
Torsten Schaub ◽  
William Yeoh
Keyword(s):  
Scale Up ◽  
Answer Set Programming ◽  
Specific Information ◽  
Path Finding ◽  
Practical Applications ◽  
Target Assignment ◽  
Specific Order ◽  
Multi Agent ◽  
Unequal Number ◽  
Answer Set

In Multi-Agent Path Finding (MAPF), a team of agents needs to find collision-free paths from their starting locations to their respective targets. Combined Target Assignment and Path Finding (TAPF) extends MAPF by including the problem of assigning targets to agents as a precursor to the MAPF problem. A limitation of both models is their assumption that the number of agents and targets are equal, which is invalid in some applications such as autonomous warehouse systems. We address this limitation by generalizing TAPF to allow for (1)~unequal number of agents and tasks; (2)~tasks to have deadlines by which they must be completed; (3)~ordering of groups of tasks to be completed; and (4)~tasks that are composed of a sequence of checkpoints that must be visited in a specific order. Further, we model the problem using answer set programming (ASP) to show that customizing the desired variant of the problem is simple one only needs to choose the appropriate combination of ASP rules to enforce it. We also demonstrate experimentally that if problem specific information can be incorporated into the ASP encoding then ASP based method can be efficient and can scale up to solve practical applications.

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.

scholarly journals Thedlvhexsystem for knowledge representation: recent advances (system description)

2016 ◽  
Vol 16 (5-6) ◽  
pp. 866-883 ◽  
Author(s):  
CHRISTOPH REDL
Keyword(s):  
Knowledge Representation ◽  
Real World ◽  
Answer Set Programming ◽  
Current Status ◽  
System Description ◽  
Recent Advances ◽  
Real World Applications ◽  
External Sources ◽  
Answer Set

AbstractThedlvhexsystem implements thehex-semantics, which integrates answer set programming (ASP) with arbitrary external sources. Since its first release ten years ago, significant advancements were achieved. Most importantly, the exploitation of properties of external sources led to efficiency improvements and flexibility enhancements of the language, and technical improvements on the system side increased user's convenience. In this paper, we present the current status of the system and point out the most important recent enhancements over early versions. While existing literature focuses on theoretical aspects and specific components, a bird's eye view of the overall system is missing. In order to promote the system for real-world applications, we further present applications which were already successfully realized on top ofdlvhex.

Systems, Engineering Environments, and Competitions

AI Magazine ◽  
2016 ◽  
Vol 37 (3) ◽  
pp. 45-52 ◽  
Author(s):  
Yuliya Lierler ◽  
Marco Maratea ◽  
Francesco Ricca
Keyword(s):  
Systems Engineering ◽  
Answer Set Programming ◽  
Practical Applications ◽  
Development Tools ◽  
History Of ◽  
Answer Set

The goal of this article is threefold. First, we trace the history of the development of answer set solvers, by accounting for more than a dozen of them. Second, we discuss development tools and environments that facilitate the use of answer set programming technology in practical applications. Last, we present the evolution of the answer set programming competitions, prime venues for tracking advances in answer set solving technology.

scholarly journals Combining Answer Set Programming and domain heuristics for solving hard industrial problems (Application Paper)

2016 ◽  
Vol 16 (5-6) ◽  
pp. 653-669 ◽  
Author(s):  
CARMINE DODARO ◽  
PHILIP GASTEIGER ◽  
NICOLA LEONE ◽  
BENJAMIN MUSITSCH ◽  
FRANCESCO RICCA ◽  
...  
Keyword(s):  
Real World ◽  
Answer Set Programming ◽  
Domain Specific ◽  
Real World Problem ◽  
Real World Applications ◽  
Application Paper ◽  
Problem Instances ◽  
Effective Integration ◽  
Configuration Problem ◽  
Answer Set

AbstractAnswer Set Programming (ASP) is a popular logic programming paradigm that has been applied for solving a variety of complex problems. Among the most challenging real-world applications of ASP are two industrial problems defined by Siemens: the Partner Units Problem (PUP) and the Combined Configuration Problem (CCP). The hardest instances of PUP and CCP are out of reach for state-of-the-art ASP solvers. Experiments show that the performance of ASP solvers could be significantly improved by embedding domain-specific heuristics, but a proper effective integration of such criteria in off-the-shelf ASP implementations is not obvious. In this paper the combination of ASP and domain-specific heuristics is studied with the goal of effectively solving real-world problem instances of PUP and CCP. As a byproduct of this activity, the ASP solverwaspwas extended with an interface that eases embedding new external heuristics in the solver. The evaluation shows that our domain-heuristic-driven ASP solver finds solutions for all the real-world instances of PUP and CCP ever provided by Siemens.

STRATEGIES TO DEFEND A PROTAGONIST OF AN EVENT

2010 ◽  
Vol 19 (04) ◽  
pp. 439-464
Author(s):  
SARA BOUTOUHAMI ◽  
DANIEL KAYSER
Keyword(s):  
Natural Language ◽  
Experimental Validation ◽  
Answer Set Programming ◽  
First Order ◽  
Starting Point ◽  
First Results ◽  
Argumentative Strategies ◽  
Complete Set ◽  
Car Accident ◽  
Answer Set

We aim at controlling the biases that exist in every description, in order to give the best possible image of one of the protagonists of an event. Starting from a supposedly complete set of propositions accounting for an event, we develop various argumentative strategies (insinuation, justification, reference to customary norms) to imply the facts that cannot be simply omitted but have the "wrong" orientation w.r.t. the protagonist we defend. By analyzing these different strategies, a contribution of this work is to provide a number of relevant parameters to take into account in developing and evaluating systems aiming at understanding natural language (NL) argumentations. The source of inspiration for this work is a corpus of 160 texts where each text describes a (different) car accident. Its result, for a given accident, is a set of first-order literals representing the essential facts of a description intended to defend one of the protagonists. An implementation in Answer Set Programming is underway. A couple of examples showing how to extract, from the same starting point, a defense for the two opposite sides are provided. Experimental validation of this work is in progress, and its first results are reported.

scholarly journals Argumentation and Defeasible Reasoning in the Law

J ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 897-914
Author(s):  
Marco Billi ◽  
Roberta Calegari ◽  
Giuseppe Contissa ◽  
Francesca Lagioia ◽  
Giuseppe Pisano ◽  
...  
Keyword(s):  
Knowledge Representation ◽  
Real World ◽  
Answer Set Programming ◽  
Defeasible Reasoning ◽  
Logical Model ◽  
Defeasible Logic ◽  
The Law ◽  
Legal Domain ◽  
Answer Set

Different formalisms for defeasible reasoning have been used to represent knowledge and reason in the legal field. In this work, we provide an overview of the following logic-based approaches to defeasible reasoning: defeasible logic, Answer Set Programming, ABA+, ASPIC+, and DeLP. We compare features of these approaches under three perspectives: the logical model (knowledge representation), the method (computational mechanisms), and the technology (available software resources). On top of that, two real examples in the legal domain are designed and implemented in ASPIC+ to showcase the benefit of an argumentation approach in real-world domains. The CrossJustice and Interlex projects are taken as a testbed, and experiments are conducted with the Arg2P technology.

Modeling Natural Language Metaphors with an Answer Set Programming Framework

2014 ◽  
pp. 28-36
Author(s):  
Juan Carlos Acosta-Guadarrama ◽  
Rogelio Dávila-Pérez ◽  
Mauricio Osorio ◽  
Victor Hugo Zaldivar
Keyword(s):  
Natural Language ◽  
Answer Set Programming ◽  
Programming Framework ◽  
Answer Set

scholarly journals Iterative Learning of Answer Set Programs from Context Dependent Examples

2016 ◽  
Vol 16 (5-6) ◽  
pp. 834-848 ◽  
Author(s):  
MARK LAW ◽  
ALESSANDRA RUSSO ◽  
KRYSIA BRODA
Keyword(s):  
Scale Up ◽  
Inductive Logic ◽  
Answer Set Programming ◽  
Background Knowledge ◽  
Learning Tasks ◽  
Large Numbers ◽  
Average Accuracy ◽  
Context Dependent ◽  
Answer Sets ◽  
Answer Set

AbstractIn recent years, several frameworks and systems have been proposed that extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for all examples; however, examples may be context-dependent. This means that some examples should be explained in the context of some information, whereas others should be explained in different contexts. In this paper, we capture this notion and present a context-dependent extension of the Learning from Ordered Answer Sets framework. In this extension, contexts can be used to further structure the background knowledge. We then propose a new iterative algorithm, ILASP2i, which exploits this feature to scale up the existing ILASP2 system to learning tasks with large numbers of examples. We demonstrate the gain in scalability by applying both algorithms to various learning tasks. Our results show that, compared to ILASP2, the newly proposed ILASP2i system can be two orders of magnitude faster and use two orders of magnitude less memory, whilst preserving the same average accuracy.

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