ML + FV = ♡\heartsuit? A Survey on the Application of Machine Learning to Formal Verification

Formal Verification (FV) and Machine Learning (ML) can seem incompatible due to their opposite mathematical foundations and their use in real-life problems: FV mostly relies on discrete mathematics and aims at ensuring correctness; ML often relies on probabilistic models and consists of learning patterns from training data. In this paper, we postulate that they are complementary in practice, and explore how ML helps FV in its classical approaches: static analysis, model-checking, theorem-proving, and SAT solving. We draw a landscape of the current practice and catalog some of the most prominent uses of ML inside FV tools, thus offering a new perspective on FV techniques that can help researchers and practitioners to better locate the possible synergies. We discuss lessons learned from our work, point to possible improvements and offer visions for the future of the domain in the light of the science of software and systems modeling.Comment: 13 pages, no figures, 3 table

An Intelligent Expert System for Decision Analysis and Support in Multi-Attribute Layout Optimization

Layout Decision Analysis and Design is a ubiquitous problem in a variety of work domains that is important from both strategic and operational perspectives. It is largely a complex, vague, difficult, and ill-structured problem that requires intelligent and sophisticated decision analysis and design support. Inadequate information availability, combinatorial complexity, subjective and uncertain preferences, and cognitive biases of decision makers often hamper the procurement of a superior layout configuration. Consequently, it is desirable to develop an intelligent decision support system for layout design that could deal with such challenging issues by providing efficient and effective means of generating, analyzing, enumerating, ranking, and manipulating superior alternative layouts. We present a research framework and a functional prototype for an interactive Intelligent System for Decision Support and Expert Analysis in Multi-Attribute Layout Optimization (IDEAL) based on soft computing tools. A fundamental issue in layout design is efficient production of superior alternatives through the incorporation of subjective and uncertain design preferences. Consequently, we have developed an efficient and Intelligent Layout Design Generator (ILG) using a generic two-dimensional bin-packing formulation that utilizes multiple preference weights furnished by a fuzzy Preference Inferencing Agent (PIA). The sub-cognitive, intuitive, multi-facet, and dynamic nature of design preferences indicates that an automated Preference Discovery Agent (PDA) could be an important component of such a system. A user-friendly, interactive, and effective User Interface is deemed critical for the success of the system. The effectiveness of the proposed solution paradigm and the implemented prototype is demonstrated through examples and cases. This research framework and prototype contribute to the field of layout decision analysis and design by enabling explicit representation of experts? knowledge, formal modeling of fuzzy user preferences, and swift generation and manipulation of superior layout alternatives. Such efforts are expected to afford efficient procurement of superior outcomes and to facilitate cognitive, ergonomic, and economic efficiency of layout designers as well as future research in related areas. Applications of this research are broad ranging including facilities layout design, VLSI circuit layout design, newspaper layout design, cutting and packing, adaptive user interfaces, dynamic memory allocation, multi-processor scheduling, metacomputing, etc

Optimizing quantum circuit layouts

Un dels problemes amb els quals s'enfronta la computació quàntica és el de l'optimització de la compilació d'un circuit quàntic. El procés de compilació inclou bàsicament dues etapes: síntesi del circuit a executar en termes de les portes quàntiques suportades pel processador, i adaptació del circuit a executar a les limitacions de connectivitat imposades pel processador. En aquest treball, he abordat el segon d'aquests problemes, conegut amb el nom de Quantum Circuit Layout (QCL). Per a la seva resolució, he intentat usar tècniques de Reinforcement Learning (RL), que requereixen modelitzar prèviament el problema en termes d'un Markov Decision Process (MDP). En concret, descric dos MDP's finits la solució dels quals proporciona una solució a una part del problema del QCL. El problema principal és dissenyar un mètode que permeti efectivament resoldre aquests MDP's, ni que sigui de manera aproximada. En el treball es discuteixen dues aproximacions al problema. La primera d'elles utilitza una variant de l'algoritme usat per AlphaZero, dissenyat amb l'objectiu d'entrenar a una màquina per tal que aprengui a jugar als jocs d'Escacs, Shogi i Go. La segona utilitza una aproximació més estàndard coneguda com a Deep Q-Learning (DQL).One of the challenges in quantum computing is the problem of optimizing quantum circuit compilation. The compilation process involves two main stages: synthesizing the circuit to be executed in terms of the quantum gates supported by the processor, and adapting the circuit to the connectivity limitations imposed by the processor. In this work, I have addressed the second of these problems, known as Quantum Circuit Layout (QCL). To tackle this problem, I have attempted to use Reiforcement Learning (RL) techniques, which require modeling the problem as a Markov Decision Process (MDP). Specifically, I describe two finite MDPs whose solution provides a solution to a part of the QCL problem. The main problem is to design a method that effectively solves these MDPs, even if it is only an approximate solution. In the thesis two approaches to the problem are discussed. The first one uses a variant of the algorithm used in AlphaZero, designed to train a machine to learn how to play Chess, Shogi, and Go. The second approach uses a more standard approximation known as Deep Q-Learning (DQL)

Artificial cognitive architecture with self-learning and self-optimization capabilities. Case studies in micromachining processes

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura : 22-09-201

ICAPS 2012. Proceedings of the third Workshop on the International Planning Competition

22nd International Conference on Automated Planning and Scheduling. June 25-29, 2012, Atibaia, Sao Paulo (Brazil). Proceedings of the 3rd the International Planning CompetitionThe Academic Advising Planning Domain / Joshua T. Guerin, Josiah P. Hanna, Libby Ferland, Nicholas Mattei, and Judy Goldsmith. -- Leveraging Classical Planners through Translations / Ronen I. Brafman, Guy Shani, and Ran Taig. -- Advances in BDD Search: Filtering, Partitioning, and Bidirectionally Blind / Stefan Edelkamp, Peter Kissmann, and Álvaro Torralba. -- A Multi-Agent Extension of PDDL3.1 / Daniel L. Kovacs. -- Mining IPC-2011 Results / Isabel Cenamor, Tomás de la Rosa, and Fernando Fernández. -- How Good is the Performance of the Best Portfolio in IPC-2011? / Sergio Nuñez, Daniel Borrajo, and Carlos Linares López. -- “Type Problem in Domain Description!” or, Outsiders’ Suggestions for PDDL Improvement / Robert P. Goldman and Peter KellerEn prens

Applications of biased-randomized algorithms and simheuristics in integrated logistics

Transportation and logistics (T&L) activities play a vital role in the development of many businesses from different industries. With the increasing number of people living in urban areas, the expansion of on-demand economy and e-commerce activities, the number of services from transportation and delivery has considerably increased. Consequently, several urban problems have been potentialized, such as traffic congestion and pollution. Several related problems can be formulated as a combinatorial optimization problem (COP). Since most of them are NP-Hard, the finding of optimal solutions through exact solution methods is often impractical in a reasonable amount of time. In realistic settings, the increasing need for 'instant' decision-making further refutes their use in real life. Under these circumstances, this thesis aims at: (i) identifying realistic COPs from different industries; (ii) developing different classes of approximate solution approaches to solve the identified T&L problems; (iii) conducting a series of computational experiments to validate and measure the performance of the developed approaches. The novel concept of 'agile optimization' is introduced, which refers to the combination of biased-randomized heuristics with parallel computing to deal with real-time decision-making.Las actividades de transporte y logística (T&L) juegan un papel vital en el desarrollo de muchas empresas de diferentes industrias. Con el creciente número de personas que viven en áreas urbanas, la expansión de la economía a lacarta y las actividades de comercio electrónico, el número de servicios de transporte y entrega ha aumentado considerablemente. En consecuencia, se han potencializado varios problemas urbanos, como la congestión del tráfico y la contaminación. Varios problemas relacionados pueden formularse como un problema de optimización combinatoria (COP). Dado que la mayoría de ellos son NP-Hard, la búsqueda de soluciones óptimas a través de métodos de solución exactos a menudo no es práctico en un período de tiempo razonable. En entornos realistas, la creciente necesidad de una toma de decisiones "instantánea" refuta aún más su uso en la vida real. En estas circunstancias, esta tesis tiene como objetivo: (i) identificar COP realistas de diferentes industrias; (ii) desarrollar diferentes clases de enfoques de solución aproximada para resolver los problemas de T&L identificados; (iii) realizar una serie de experimentos computacionales para validar y medir el desempeño de los enfoques desarrollados. Se introduce el nuevo concepto de optimización ágil, que se refiere a la combinación de heurísticas aleatorias sesgadas con computación paralela para hacer frente a la toma de decisiones en tiempo real.Les activitats de transport i logística (T&L) tenen un paper vital en el desenvolupament de moltes empreses de diferents indústries. Amb l'augment del nombre de persones que viuen a les zones urbanes, l'expansió de l'economia a la carta i les activitats de comerç electrònic, el nombre de serveis del transport i el lliurament ha augmentat considerablement. En conseqüència, s'han potencialitzat diversos problemes urbans, com ara la congestió del trànsit i la contaminació. Es poden formular diversos problemes relacionats com a problema d'optimització combinatòria (COP). Com que la majoria són NP-Hard, la recerca de solucions òptimes mitjançant mètodes de solució exactes sovint no és pràctica en un temps raonable. En entorns realistes, la creixent necessitat de prendre decisions "instantànies" refuta encara més el seu ús a la vida real. En aquestes circumstàncies, aquesta tesi té com a objectiu: (i) identificar COP realistes de diferents indústries; (ii) desenvolupar diferents classes d'aproximacions aproximades a la solució per resoldre els problemes identificats de T&L; (iii) la realització d'una sèrie d'experiments computacionals per validar i mesurar el rendiment dels enfocaments desenvolupats. S'introdueix el nou concepte d'optimització àgil, que fa referència a la combinació d'heurístiques esbiaixades i aleatòries amb informàtica paral·lela per fer front a la presa de decisions en temps real.Tecnologies de la informació i de xarxe

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence