Marcello Balduccini

Tran Cao Son, Enrico Pontelli, Marcello Balduccini et al.

Answer Set Planning refers to the use of Answer Set Programming (ASP) to compute plans, i.e., solutions to planning problems, that transform a given state of the world to another state. The development of efficient and scalable answer set solvers has provided a significant boost to the development of ASP-based planning systems. This paper surveys the progress made during the last two and a half decades in the area of answer set planning, from its foundations to its use in challenging planning domains. The survey explores the advantages and disadvantages of answer set planning. It also discusses typical applications of answer set planning and presents a set of challenges for future research.

Thanh Hai Nguyen, Matthew Bundas, Tran Cao Son et al.

This paper introduces a formal definition of a Cyber-Physical System (CPS) in the spirit of the CPS Framework proposed by the National Institute of Standards and Technology (NIST). It shows that using this definition, various problems related to concerns in a CPS can be precisely formalized and implemented using Answer Set Programming (ASP). These include problems related to the dependency or conflicts between concerns, how to mitigate an issue, and what the most suitable mitigation strategy for a given issue would be. It then shows how ASP can be used to develop an implementation that addresses the aforementioned problems. The paper concludes with a discussion of the potentials of the proposed methodologies.

Ly Ly Trieu, Tran Cao Son, Enrico Pontelli et al.

We present an explanation system for applications that leverage Answer Set Programming (ASP). Given a program P, an answer set A of P, and an atom a in the program P, our system generates all explanation graphs of a which help explain why a is true (or false) given the program P and the answer set A. We illustrate the functionality of the system using some examples from the literature.

Daniela Inclezan, Qinglin Zhang, Marcello Balduccini et al.

We describe an application of Answer Set Programming to the understanding of narratives about stereotypical activities, demonstrated via question answering. Substantial work in this direction was done by Erik Mueller, who modeled stereotypical activities as scripts. His systems were able to understand a good number of narratives, but could not process texts describing exceptional scenarios. We propose addressing this problem by using a theory of intentions developed by Blount, Gelfond, and Balduccini. We present a methodology in which we substitute scripts by activities (i.e., hierarchical plans associated with goals) and employ the concept of an intentional agent to reason about both normal and exceptional scenarios. We exemplify the application of this methodology by answering questions about a number of restaurant stories. This paper is under consideration for acceptance in TPLP.

Marcello Balduccini, Daniele Magazzeni, Marco Maratea

PDDL+ is an extension of PDDL that enables modelling planning domains with mixed discrete-continuous dynamics. In this paper we present a new approach to PDDL+ planning based on Constraint Answer Set Programming (CASP), i.e. ASP rules plus numerical constraints. To the best of our knowledge, ours is the first attempt to link PDDL+ planning and logic programming. We provide an encoding of PDDL+ models into CASP problems. The encoding can handle non-linear hybrid domains, and represents a solid basis for applying logic programming to PDDL+ planning. As a case study, we consider the EZCSP CASP solver and obtain promising results on a set of PDDL+ benchmark problems.

Marcello Balduccini, Yulia Lierler

Recently, researchers in answer set programming and constraint programming spent significant efforts in the development of hybrid languages and solving algorithms combining the strengths of these traditionally separate fields. These efforts resulted in a new research area: constraint answer set programming (CASP). CASP languages and systems proved to be largely successful at providing efficient solutions to problems involving hybrid reasoning tasks, such as scheduling problems with elements of planning. Yet, the development of CASP systems is difficult, requiring non-trivial expertise in multiple areas. This suggests a need for a study identifying general development principles of hybrid systems. Once these principles and their implications are well understood, the development of hybrid languages and systems may become a well-established and well-understood routine process. As a step in this direction, in this paper we conduct a case study aimed at evaluating various integration schemas of CASP methods.