A NATURALISTIC COMPUTATIONAL MODEL OF HUMAN BEHAVIOR IN NAVIGATION AND SEARCH TASKS

Planning, navigation, and search are fundamental human cognitive abilities central to spatial problem solving in search and rescue, law enforcement, and military operations. Despite a wealth of literature concerning naturalistic spatial problem solving in animals, literature on naturalistic spatial problem solving in humans is comparatively lacking and generally conducted by separate camps among which there is little crosstalk. Addressing this deficiency will allow us to predict spatial decision making in operational environments, and understand the factors leading to those decisions. The present dissertation is comprised of two related efforts, (1) a set of empirical research studies intended to identify characteristics of planning, execution, and memory in naturalistic spatial problem solving tasks, and (2) a computational modeling effort to develop a model of naturalistic spatial problem solving. The results of the behavioral studies indicate that problem space hierarchical representations are linear in shape, and that human solutions are produced according to multiple optimization criteria. The Mixed Criteria Model presented in this dissertation accounts for global and local human performance in a traditional and naturalistic Traveling Salesman Problem. The results of the empirical and modeling efforts hold implications for basic and applied science in domains such as problem solving, operations research, human-computer interaction, and artificial intelligence

Preventing premature convergence and proving the optimality in evolutionary algorithms

http://ea2013.inria.fr//proceedings.pdfInternational audienceEvolutionary Algorithms (EA) usually carry out an efficient exploration of the search-space, but get often trapped in local minima and do not prove the optimality of the solution. Interval-based techniques, on the other hand, yield a numerical proof of optimality of the solution. However, they may fail to converge within a reasonable time due to their inability to quickly compute a good approximation of the global minimum and their exponential complexity. The contribution of this paper is a hybrid algorithm called Charibde in which a particular EA, Differential Evolution, cooperates with a Branch and Bound algorithm endowed with interval propagation techniques. It prevents premature convergence toward local optima and outperforms both deterministic and stochastic existing approaches. We demonstrate its efficiency on a benchmark of highly multimodal problems, for which we provide previously unknown global minima and certification of optimality

Risk-Aware Planning for Sensor Data Collection

With the emergence of low-cost unmanned air vehicles, civilian and military organizations are quickly identifying new applications for affordable, large-scale collectives to support and augment human efforts via sensor data collection. In order to be viable, these collectives must be resilient to the risk and uncertainty of operating in real-world environments. Previous work in multi-agent planning has avoided planning for the loss of agents in environments with risk. In contrast, this dissertation presents a problem formulation that includes the risk of losing agents, the effect of those losses on the mission being executed, and provides anticipatory planning algorithms that consider risk. We conduct a thorough analysis of the effects of risk on path-based planning, motivating new solution methods. We then use hierarchical clustering to generate risk-aware plans for a variable number of agents, outperforming traditional planning methods. Next, we provide a mechanism for distributed negotiation of stable plans, utilizing coalitional game theory to provide cost allocation methods that we prove to be fair and stable. Centralized planning with redundancy is then explored, planning for parallel task completion to mitigate risk and provide further increased expected value. Finally, we explore the role of cost uncertainty as additional source of risk, using bi-objective optimization to generate sets of alternative plans. We demonstrate the capability of our algorithms on randomly generated problem instances, showing an improvement over traditional multi-agent planning methods as high as 500% on very large problem instances

Crowdsensing-driven route optimisation algorithms for smart urban mobility

Urban rörlighet anses ofta vara en av de främsta möjliggörarna för en hållbar statsutveckling. Idag skulle det dock kräva ett betydande skifte mot renare och effektivare stadstransporter vilket skulle stödja ökad social och ekonomisk koncentration av resurser i städerna. En viktig prioritet för städer runt om i världen är att stödja medborgarnas rörlighet inom stadsmiljöer medan samtidigt minska trafikstockningar, olyckor och föroreningar. Att utveckla en effektivare och grönare (eller med ett ord; smartare) stadsrörlighet är en av de svåraste problemen att bemöta för stora metropoler. I denna avhandling närmar vi oss problemet från det snabba utvecklingsperspektivet av ITlandskapet i städer vilket möjliggör byggandet av rörlighetslösningar utan stora stora investeringar eller sofistikerad sensortenkik. I synnerhet föreslår vi utnyttjandet av den mobila rörlighetsavkännings, eng. Mobile Crowdsensing (MCS), paradigmen i vilken befolkningen exploaterar sin mobilkommunikation och/eller mobilasensorer med syftet att frivilligt samla, distribuera, lokalt processera och analysera geospecifik information. Rörlighetavkänningssdata (t.ex. händelser, trafikintensitet, buller och luftföroreningar etc.) inhämtad från frivilliga i befolkningen kan ge värdefull information om aktuella rörelsesförhållanden i stad vilka, med adekvata databehandlingsalgoriter, kan användas för att planera människors rörelseflöden inom stadsmiljön. Såtillvida kombineras i denna avhandling två mycket lovande smarta rörlighetsmöjliggörare, eng. Smart Mobility Enablers, nämligen MCS och rese/ruttplanering. Vi kan därmed till viss utsträckning sammanföra forskningsutmaningar från dessa två delar. Vi väljer att separera våra forskningsmål i två delar, dvs forskningssteg: (1) arkitektoniska utmaningar vid design av MCS-system och (2) algoritmiska utmaningar för tillämpningar av MCS-driven ruttplanering. Vi ämnar att visa en logisk forskningsprogression över tiden, med avstamp i mänskligt dirigerade rörelseavkänningssystem som MCS och ett avslut i automatiserade ruttoptimeringsalgoritmer skräddarsydda för specifika MCS-applikationer. Även om vi förlitar oss på heuristiska lösningar och algoritmer för NP-svåra ruttproblem förlitar vi oss på äkta applikationer med syftet att visa på fördelarna med algoritm- och infrastrukturförslagen.La movilidad urbana es considerada una de las principales desencadenantes de un desarrollo urbano sostenible. Sin embargo, hoy en día se requiere una transición hacia un transporte urbano más limpio y más eficiente que soporte una concentración de recursos sociales y económicos cada vez mayor en las ciudades. Una de las principales prioridades para las ciudades de todo el mundo es facilitar la movilidad de los ciudadanos dentro de los entornos urbanos, al mismo tiempo que se reduce la congestión, los accidentes y la contaminación. Sin embargo, desarrollar una movilidad urbana más eficiente y más verde (o en una palabra, más inteligente) es uno de los temas más difíciles de afrontar para las grandes áreas metropolitanas. En esta tesis, abordamos este problema desde la perspectiva de un panorama TIC en rápida evolución que nos permite construir movilidad sin la necesidad de grandes inversiones ni sofisticadas tecnologías de sensores. En particular, proponemos aprovechar el paradigma Mobile Crowdsensing (MCS) en el que los ciudadanos utilizan sus teléfonos móviles y dispositivos, para nosotros recopilar, procesar y analizar localmente información georreferenciada, distribuida voluntariamente. Los datos de movilidad recopilados de ciudadanos que voluntariamente quieren compartirlos (por ejemplo, eventos, intensidad del tráfico, ruido y contaminación del aire, etc.) pueden proporcionar información valiosa sobre las condiciones de movilidad actuales en la ciudad, que con el algoritmo de procesamiento de datos adecuado, pueden utilizarse para enrutar y gestionar el flujo de gente en entornos urbanos. Por lo tanto, en esta tesis combinamos dos prometedoras fuentes de movilidad inteligente: MCS y la planificación de viajes/rutas, uniendo en cierta medida los distintos desafíos de investigación. Hemos dividido nuestros objetivos de investigación en dos etapas: (1) Desafíos arquitectónicos en el diseño de sistemas MCS y (2) Desafíos algorítmicos en la planificación de rutas aprovechando la información del MCS. Nuestro objetivo es demostrar una progresión lógica de la investigación a lo largo del tiempo, comenzando desde los fundamentos de los sistemas de detección centrados en personas, como el MCS, hasta los algoritmos de optimización de rutas diseñados específicamente para la aplicación de estos. Si bien nos centramos en algoritmos y heurísticas para resolver problemas de enrutamiento de clase NP-hard, utilizamos ejemplos de aplicaciones en el mundo real para mostrar las ventajas de los algoritmos e infraestructuras propuestas

Decomposition strategies for large scale multi depot vehicle routing problems

Das Umfeld in der heutigen Wirtschaft verlangt nach immer bessern Ansätzen, um Transportprobleme möglichst effizient zu lösen. Die Klasse der ”Vehicle Routing Problems” (VRP) beschäftigt sich speziell mit der Optimierung von Tourenplanungsproblemen in dem ein Service-Leister seine Kunden möglichst effizient beliefern muss. Eine der VRP-Varianten ist das ”Multi Depot Vehicle Routing Problem with Time Windows” (MDVRPTW), in dem Kunden von verschiedenen Depots in einem fix vorgegebenen Zeitintervall beliefert beliefert werden müssen. Das MDVRPTW ist im realen Leben dank seiner realitätsnahen Restriktionen sehr oft vertreten. Typische Transportprobleme, wie sie in der Wirklichkeit auftreten, sind jedoch oftmals so groß, dass sie von optimalen Lösungsansätzen nicht zufriedenstellend gelöst werden können. In der vorliegenden Dissertation werden zwei Lösungsansätze präsentiert, wie diese riesigen, realitätsnahen Probleme zufriedenstellend bewältigt werden können. Beide Ansätze benutzen die POPMUSIC Grundstruktur, um das Problem möglichst intelligent zu dekomponieren. Die Dekomponierten und damit kleineren Subprobleme können dann von speziell entwickelten Algorithmen effizienter bearbeitet und letztendlich gelöst werden. Mit dem ersten Ansatz präsentieren wir eine Möglichkeit Transportprobleme zu dekomponieren, wenn populationsbasierte Algorithmen als Problemlöser eingesetzt werden. Dazu wurde ein maßgeschneiderter Memetischer Algorithmus (MA) entwickelt und in das Dekompositionsgerüst eingebaut um ein reales Problem eines österreichischen Transportunternehmens zu lösen. Wir zeigen, dass die Dekomponierung und Optimierung der resultierenden Subprobleme, im Vergleich zu den Ergebnissen des MA ohne Dekomposition, eine Verbesserung der Zielfunktion von rund 20% ermöglicht. Der zweite Ansatz beschäftigt sich mit der Entwicklung einer Dekomponierungsmethode für Lösungsalgorithmen, die nur an einer einzigen Lösung arbeiten. Es wurde ein ”Variable Neigborhood Search” (VNS) als Optimierer in das POPMUSIC Grundgerüst implementiert, um an das vorhandene Echtwelt-Problem heranzugehen. Wir zeigen, dass dieser Ansatz rund 7% bessere Ergebnisse liefert als der pure VNS Lösungsansatz. Außerdem präsentieren wir Ergebnisse des VNS Dekompositionsansatzes die um rund 6% besser sind als die des MA Dekompositionsansatzes. Ein weiterer Beitrag dieser Arbeit ist das Vorstellen von zwei komplett verschiedenen Ansätzen um das Problem in kleinere Sub-Probleme zu zerteilen. Dazu wurden acht verschiedene Nähe-Maße definiert und betrachtet. Es wurde der 2,3 und 4 Depot Fall getestet und im Detail analysiert. Die Ergebnisse werden präsentiert und wir stellen einen eindeutigen Gewinner vor, der alle Testinstanzen am Besten lösen konnte. Wir weisen auch darauf hin, wie einfach die POPMUSIC Dekomponierung an reale Bedürfnisse, wie zum Beispiel eine möglichst schnelle Ergebnisgenerierung, angepasst werden kann. Wir zeigen damit, dass die vorgestellten Dekomponierungsstrategien sehr effizient und flexibel sind, wenn Transportprobleme, wie sie in der realen Welt vorkommen gelöst werden müssen.The optimization of transportation activities is of high importance for companies in today’s economy. The Vehicle Routing Problem (VRP) class is dealing with the routing of vehicles so that the customer base of a company can be served in the most efficient way. One of the many variants in the VRP class is the Multi Depot Vehicle Routing Problem with Time Windows (MDVRPTW) which extends the VRP by additional depots from which customers can be served, as well as an individual time window for each customer in which he is allowed to be served. Modern carrier fleet operators often encounter these MDVRPTW in the real world, and usually they are of very large size so that exact approaches cannot solve them efficiently. This thesis presents two different approaches how this real world large scale MDVRPTWs can be solved. Both approaches are based on the POPMUSIC framework, which intelligently tries to decompose the large scale problem into much smaller sub-problems. The resulting sub-problems can then be solved more efficiently by specialized optimizers. The first approach in this thesis was developed for population based optimizers. A Memetic Algorithm (MA) was developed and used as an optimizer in the framework to solve a real world MDVPRTW from an Austrian carrier fleet operator. We show that decomposing the complete problem and solving the resulting sub-problems improves the solution quality by around 20% compared to using the MA without any decomposition. The second approach specially focuses on decomposition strategies for single solution methods. More precisely, a Variable Neighborhood Search (VNS) was implemented in the POPMUSIC framework to solve the real world instances. We show that decomposing the problem can yield improvements of around 7% compared to using the pure VNS method. Compared to the POPMUSIC MA approach the second approach can further improve the solution quality by around 6%. Another contribution in this thesis is the development of two generally different ways to measure proximity when creating sub-problems. In detail we tested eight different proximity measures and analyzed how good they decompose the problem in different environments. We tested the two, three and four depot case and present a clear winner that can outperform all other measures. Further we demonstrate that the POPMUSIC approach can flexibly be adjusted to real world demands, like a faster solution finding process, while at the same time maintaining high quality solutions. We show that a decomposition strategies combined with state of the art metaheuristic solvers are a very efficient and flexible tool to tackle real world problems with regards to solution quality as well as runtime

How Good Is Neural Combinatorial Optimization?

Traditional solvers for tackling combinatorial optimization (CO) problems are usually designed by human experts. Recently, there has been a surge of interest in utilizing Deep Learning, especially Deep Reinforcement Learning, to automatically learn effective solvers for CO. The resultant new paradigm is termed Neural Combinatorial Optimization (NCO). However, the advantages and disadvantages of NCO over other approaches have not been well studied empirically or theoretically. In this work, we present a comprehensive comparative study of NCO solvers and alternative solvers. Specifically, taking the Traveling Salesman Problem as the testbed problem, we assess the performance of the solvers in terms of five aspects, i.e., effectiveness, efficiency, stability, scalability and generalization ability. Our results show that in general the solvers learned by NCO approaches still fall short of traditional solvers in nearly all these aspects. A potential benefit of the former would be their superior time and energy efficiency on small-size problem instances when sufficient training instances are available. We hope this work would help better understand the strengths and weakness of NCO, and provide a comprehensive evaluation protocol for further benchmarking NCO approaches against other approaches

Traveling Salesman Problem

This book is a collection of current research in the application of evolutionary algorithms and other optimal algorithms to solving the TSP problem. It brings together researchers with applications in Artificial Immune Systems, Genetic Algorithms, Neural Networks and Differential Evolution Algorithm. Hybrid systems, like Fuzzy Maps, Chaotic Maps and Parallelized TSP are also presented. Most importantly, this book presents both theoretical as well as practical applications of TSP, which will be a vital tool for researchers and graduate entry students in the field of applied Mathematics, Computing Science and Engineering