Learning Generalized Reactive Policies using Deep Neural Networks

We present a new approach to learning for planning, where knowledge acquired while solving a given set of planning problems is used to plan faster in related, but new problem instances. We show that a deep neural network can be used to learn and represent a \emph{generalized reactive policy} (GRP) that maps a problem instance and a state to an action, and that the learned GRPs efficiently solve large classes of challenging problem instances. In contrast to prior efforts in this direction, our approach significantly reduces the dependence of learning on handcrafted domain knowledge or feature selection. Instead, the GRP is trained from scratch using a set of successful execution traces. We show that our approach can also be used to automatically learn a heuristic function that can be used in directed search algorithms. We evaluate our approach using an extensive suite of experiments on two challenging planning problem domains and show that our approach facilitates learning complex decision making policies and powerful heuristic functions with minimal human input. Videos of our results are available at goo.gl/Hpy4e3

Dynamic vehicle routing problems: Three decades and counting

Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.© 2015 Wiley Periodicals, Inc

Análise de Performance de Técnicas de Optimização

Real-world complex optimization problems are one of the most complex challenges faced by scientific community. Achieving the best solution for a complex problem in an acceptable time interval is not always possible. In order to solve this problem, metaheuristics are one of the available resources. Having this in mind, finding a technique among others that presents better results in most executions would allow solution choosing to be more directive and assertive. Most used techniques comprise metaheuristics. These allow to find an acceptable solution in an acceptable time interval, even if the achieved solution was not the optimal possible. In this sense, this thesis intends to analyse four optimization techniques. Two population based techniques, one of them based in the behaviour of the bees in colony (Bee Colony) and another based in computational evolution (Genetic Algorithms). And, two single solution techniques, one based in memory structures (Tabu Search) and another based in the metallurgy industry (Simulated Annealing). These techniques were applied to two different optimization problems and computational results were registered and analysed. A prototype was built and used to obtain the results of applying metaheuristics to the Travelling Salesman problem (TSP) and the Knapsack Problem (KP). Evaluating the results, it was not possible to prove either that all algorithms are equivalent or that one of them is better in the majority of the cases.A resolução de problemas de otimização reais complexos constitui um dos grandes desafios científicos atuais. A possibilidade de obter as melhores soluções para os problemas nem sempre é possível em tempo útil e o recurso a técnicas de otimização para os resolver de forma eficaz e eficiente é constante. Neste sentido, encontrar uma técnica que sobressaia por entre as demais permitiria usar essas técnicas de forma mais direcionada e assertiva. Algumas das técnicas de otimização mais usadas são as meta-heurísticas. Estas permitem encontrar uma solução em tempo útil, mesmo não sendo a melhor solução possível. Neste contexto, a presente dissertação tem por vista a análise de quatro técnicas de otimização. Duas populacionais, sendo que uma técnica é baseada no comportamento dos enxames de abelhas (Bee Colony) e outra baseada na computação evolucionária, algoritmos genéticos (Genetic Algorithms). E, por posição, duas de solução única, a pesquisa tabu (Tabu Search), que se baseia nas estruturas de memória e uma técnica baseada na indústria metalúrgica, o arrefecimento simulado (Simulated Anealing). Estas técnicas foram aplicadas a dois problemas de otimização e os resultados computacionais, eficiência e eficácia das técnicas, foram registados e analisados. Um protótipo foi construído e utilizado para obter os resultados da aplicação das metaheurísticas ao problema de caixeiro viajante (TSP) e ao problema da mochila (KP). Após avaliação dos resultados, não foi possível provar que existia um algoritmo que se destacava entre os demais ou que os algoritmos eram equivalentes

Soft range information for network localization

The demand for accurate localization in complex environments continues to increase despite the difficulty in extracting positional information from measurements. Conventional range-based localization approaches rely on distance estimates obtained from measurements (e.g., delay or strength of received waveforms). This paper goes one step further and develops localization techniques that rely on all probable range values rather than on a single estimate of each distance. In particular, the concept of soft range information (SRI) is introduced, showing its essential role for network localization. We then establish a general framework for SRI-based localization and develop algorithms for obtaining the SRI using machine learning techniques. The performance of the proposed approach is quantified via network experimentation in indoor environments. The results show that SRI-based localization techniques can achieve performance approaching the Cramer–Rao lower bound and significantly outperform the conventional techniques especially in harsh wireless environments.RYC-2016-1938

A new node-shift encoding representation for the travelling salesman problem

This paper presents a new genetic algorithm encoding representation to solve the travelling salesman problem. To assess the performance of the proposed chromosome structure, we compare it with state-of-the-art encoding representations. For that purpose, we use 14 benchmarks of different sizes taken from TSPLIB. Finally, after conducting the experimental study, we report the obtained results and draw our conclusion.Comment: 6 pages, 5 figures. Accepted in ICL2022, Jeddah, Saudi Arabia conference (postponed to 2024