CLP Lab

Si è svolto a Parma il 3 e 4 giugno il primo meeting di progetto INDAM/GNCS 2019 “Logic Programming for early detection of pancreatic cancer”. Mettiamocela tutta per aiutare la ricerca sul cancro!

Il 14 Maggio 2019 siamo stati invitati a presentare i nostri risultati di ragionamento automatico in sistemi multiagente applicati alle smat grid energetiche al convegno organizzato dall’academy of sciences for the developing world http://festivalsvilupposostenibile.it/ I lucidi della presentazione sono di seguito.

Distributed Multi-Agent Optimization for SmartGrids and Home Automation. Ferdinando Fioretto, Agostino Dovier, Enrico Pontelli. IA 12(2):67-87 (2018)

Abstract

Distributed Constraint Optimization Problems (DCOPs) have emerged as one of the prominent multi-agent architectures to govern the agents’autonomous behavior in a cooperative multi-agent system (MAS) where several agents coordinate with each other to optimize a global cost function taking into account their local preferences. They represent a powerful approach to the description and resolution of many practical problems. However, typical MAS applications are characterized by complex dynamics and interactions among a large number of entities, which translate into hard combinatorial problems, posing significant challenges from a computational and coordination standpoints. This paper reviews two methods to promote a hierarchical parallelmodel for solving DCOPs, with the aim of improving the performance ofthe DCOP algorithm. The first is aMulti-Variable Agent (MVA) DCOP decomposition, which exploits co-locality of an agent’s variables allowingthe adoption of efficient centralized techniques to solve the subproblem of an agent. The second is the use of Graphics Processing Units (GPUs) to speed up a class of DCOP algorithms. Finally, exploiting these hierarchical parallel model, the paper presentstwo critical applications of DCOPs for demand response(DR) programin smart grids. The Multi-agent Economic Dispatch with Demand Re-sponse (EDDR), which provides an integrated approach to the economic dispatch and the DR model for power systems, and the Smart Home De-vice Scheduling (SHDS) problem, that formalizes the device scheduling and coordination problem across multiple smart homes to reduce energy peaks.

Preprint

Alessandro Dal Palù, Agostino Dovier, Andrea Formisano, Enrico Pontelli:
ASP Applications in Bio-informatics: A Short Tour.
Künstliche Intelligenz, Volume 32 (2-3): 157-164, 2018.

ABSTRACT

We report on how the declarative nature of Answer Set Programming allows one to model and solve some well-known and challenging classes of problems in the general domain of bioinformatics. We briefly survey the main results appeared in the areas of genomics, structure prediction, and systems biology.

https://link.springer.com/article/10.1007%2Fs13218-018-0551-y

CLPlab members Agostino Dovier, Andrea Formisano, and Enrico Pontelli have written the survey paper: Parallel Answer Set Programming, appeared as Chapter 7 of the Handbook of Parallel Constraint Reasoning: 237-282, Springer Verlag, 2018

Nando, with his dissertation entitled: Exploiting the Structure of Distributed Constraint Optimization Problems won the PhD award of the italian association for artificial intelligence.

Here all details:

http://qui.uniud.it/notizieEventi/studio-e-lavoro/assegnato-il-premio-per-neodottori-di-ricerca-dellassociazione-italiana-di-intelligenza-artificiale-ai-ia

Congratulations to Nando!!!

Agostino Dovier had the honor and pleasure of giving an invited talk to the joint conferences SAT/ICLP/CP in Melbourne.

Here there are all the slides:

DOVIER_CPSATICLP2017

Modeling and solving planning problems in tabled logic programming: Experience from the Cave Diving domain
By
Roman Bartak, Lukas Chrpa, Agostino Dovier, Jindrich Vodrazka, Neng-Fa Zhou

Abstract
Action planning deals with the problem of finding a sequence of actions transferring the world from a given state to a desired (goal) state. This problem is important in various areas such as robotics, manufacturing, transportation, autonomic computing, computer games, etc. Action planning is a form of a reachability problem in a huge state space so it is critical to efficiently represent world states and actions (transitions between states).
In this paper we present a modeling framework for planning problems based on tabled logic programming that exploits a planner module in the Picat language. In particular, we suggest techniques for structured representation of states and for including control knowledge in the description of actions.

We demonstrate these techniques using the complex planning domain Cave Diving from the International Planning Competition. Experimentally, we show properties of the model for different search approaches and we compare the performance of the proposed approach with state-of-the-art automated planners. The focus of this paper is on providing guidelines for manual modeling of planning domains rather than on automated reformulation of models.

To Appear In Science of Computer Programming

Agostino Dovier gave an invited lecture on ASP and Bioinformatics at TAASP workshop in Klagenfurt, a satellite event of INFORMATIK 2016 http://www.kr.tuwien.ac.at/events/taasp16/prog.html

Thanks to the organizers (and to all CLPLAB members for their work)

On March 17, 2016, David Luebke, NVIDIA Distinguished Inventor, and Sr. Director of Research of NVIDIA Corporation announced that we are confirmed as a GPU Research Center “based on the vision, quality, and impact of your research leveraging GPU Computing.”