Journal Paper (AI)

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.


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.

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Parallel Answer Set Programming

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


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Award for Ferdinando Fioretto

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

Screen Shot 2017-11-17 at 7.55.35 AM

Here all details:

Congratulations to Nando!!!

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Invited talk in Melbourne

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:


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New Journal Paper

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

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 diff erent 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

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Invited talk at TAASP

Agostino Dovier gave an invited lecture on ASP and Bioinformatics at TAASP workshop in Klagenfurt, a satellite event of INFORMATIK 2016

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


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Renewal from NVIDIA

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.”

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To appear in proc of PADL 2016

A GPU Implementation of the ASP Computation

By Agostino Dovier, Andrea Formisano, Enrico Pontelli, and Flavio Vella

General Purpose Graphical Processing Units (GPUs) are affordable multi-core platforms, providing access to large number of cores, but at the price of a complex architecture with non-trivial synchronization and communication costs.
This paper presents the design and implementation of a conflict-driven ASP solver, that is capable of exploiting the parallelism offered by GPUs.
The proposed system builds on the notion of ASP computation, that avoids the generation of unfounded sets, enhanced by conflict analysis and learning. The proposed system uses the CPU exclusively for input and output, in order to reduce the negative impact of the expensive data transfers between the CPU and the GPU. All the solving components, i.e., the management of nogoods, the search strategy, backjumping, the search heuristics, conflict analysis and learning, and unit propagation, are performed on the GPU, by exploiting Single Instruction Multiple Threads (SIMT) parallelism. The preliminary experimental results confirm the feasibility and scalability of the approach, and the potential to enhance performance of ASP solvers

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Invited talk @ CP 2015 in Cork

Tutorial on CP and Bioinformatics of September 4, 2015

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Greetings from Genova

From left to right: Nando Fioretto, Agostino Dovier, Enrico Pontelli, and Alessandro Dal Palù (all but one teachers at the GULP school on logic programming)


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