Manuel Carro

Joaquín Arias, Manuel Carro, Gopal Gupta

Goal-directed evaluation of Answer Set Programs is gaining traction thanks to its amenability to create AI systems that can, due to the evaluation mechanism used, generate explanations and justifications. s(CASP) is one of these systems and has been already used to write reasoning systems in several fields. It provides enhanced expressiveness w.r.t. other ASP systems due to its ability to use constraints, data structures, and unbound variables natively. However, the performance of existing s(CASP) implementations is not on par with other ASP systems: model consistency is checked once models have been generated, in keeping with the generate-and-test paradigm. In this work, we present a variation of the top-down evaluation strategy, termed Dynamic Consistency Checking, which interleaves model generation and consistency checking. This makes it possible to determine when a literal is not compatible with the denials associated to the global constraints in the program, prune the current execution branch, and choose a different alternative. This strategy is specially (but not exclusively) relevant in problems with a high combinatorial component. We have experimentally observed speedups of up to 90x w.r.t. the standard versions of s(CASP).

Joaquín Arias, Manuel Carro, Zhuo Chen et al.

Automated commonsense reasoning is essential for building human-like AI systems featuring, for example, explainable AI. Event Calculus (EC) is a family of formalisms that model commonsense reasoning with a sound, logical basis. Previous attempts to mechanize reasoning using EC faced difficulties in the treatment of the continuous change in dense domains (e.g., time and other physical quantities), constraints among variables, default negation, and the uniform application of different inference methods, among others. We propose the use of s(CASP), a query-driven, top-down execution model for Predicate Answer Set Programming with Constraints, to model and reason using EC. We show how EC scenarios can be naturally and directly encoded in s(CASP) and how it enables deductive and abductive reasoning tasks in domains featuring constraints involving both dense time and dense fluents.

Guillermo Vigueras, Manuel Carro, Salvador Tamarit et al.

The current trends in next-generation exascale systems go towards integrating a wide range of specialized (co-)processors into traditional supercomputers. Due to the efficiency of heterogeneous systems in terms of Watts and FLOPS per surface unit, opening the access of heterogeneous platforms to a wider range of users is an important problem to be tackled. However, heterogeneous platforms limit the portability of the applications and increase development complexity due to the programming skills required. Program transformation can help make programming heterogeneous systems easier by defining a step-wise transformation process that translates a given initial code into a semantically equivalent final code, but adapted to a specific platform. Program transformation systems require the definition of efficient transformation strategies to tackle the combinatorial problem that emerges due to the large set of transformations applicable at each step of the process. In this paper we propose a machine learning-based approach to learn heuristics to define program transformation strategies. Our approach proposes a novel combination of reinforcement learning and classification methods to efficiently tackle the problems inherent to this type of systems. Preliminary results demonstrate the suitability of this approach.

Salvador Tamarit, Julio Mariño, Guillermo Vigueras et al.

Obtaining good performance when programming heterogeneous computing platforms poses significant challenges. We present a program transformation environment, implemented in Haskell, where architecture-agnostic scientific C code with semantic annotations is transformed into functionally equivalent code better suited for a given platform. The transformation steps are represented as rules that can be fired when certain syntactic and semantic conditions are fulfilled. These rules are not hard-wired into the rewriting engine: they are written in a C-like language and are automatically processed and incorporated into the rewriting engine. That makes it possible for end-users to add their own rules or to provide sets of rules that are adapted to certain specific domains or purposes.

Salvador Tamarit, Julio Mariño, Guillermo Vigueras et al.

This volume contains the proceedings of PROHA 2016, the first workshop on Program Transformation for Programmability in Heterogeneous Architectures, held on March 12, 2016 in Barcelona, Spain, as an affiliated workshop of CGO 2016, the 14th International Symposium on Code Generation and Optimization. Developing and maintaining high-performance applications and libraries for heterogeneous architectures while preserving its semantics and with a reasonable efficiency is a time-consuming task which is often only possible for experts. It often requires manually adapting sequential, platform-agnostic code to different infrastructures, and keeping the changes in all of these infrastructures in sync. These program modification tasks are costly and error-prone. Tools to assist in and, if possible, automate such transformations are of course of great interest. However, such tools may need significant reasoning and knowledge processing capabilities, including, for example, being able to process machine-understandable descriptions of the semantics of a piece of code is expected to do; to perform program transformations inside a context in which they are applicable; to use strategies to identify the sequence of transformations leading to the best resulting code; and others.