Towards self-organizing logistics in transportation:a literature review and typology

Deploying self-organizing systems is a way to cope with the logistics sector's complex, dynamic, and stochastic nature. In such systems, automated decision-making and decentralized or distributed control structures are combined. Such control structures reduce the complexity of decision-making, require less computational effort, and are therefore faster, reducing the risk that changes during decision-making render the solution invalid. These benefits of self-organizing systems are of interest to many practitioners involved in solving real-world problems in the logistics sector. This study, therefore, identifies and classifies research related to self-organizing logistics (SOL) with a focus on transportation. SOL is an interdisciplinary study across many domains and relates to other concepts, such as agent-based systems, autonomous control, and decentral systems. Yet, few papers directly identify this as self-organization. Hence, we add to the existing literature by conducting a systematic literature review that provides insight into the field of SOL. The main contribution of this paper is two-fold: (i) based on the findings from the literature review, we identify and synthesize 15 characteristics of SOL in a typology, and (ii) we present a two-dimensional SOL framework alongside the axes of autonomy and cooperativity to position and contrast the broad range of literature, thereby creating order in the field of SOL and revealing promising research directions.</p

Exploring the resource recovery potentials of municipal solid waste: a review of solid wastes composting in developing countries

Population explosion, high urbanization and improved living standards have induced rapid changes in quantities and materiacompositions of solid waste generation globally. Until recently solid waste disposal in landfills and open dump sites waconsidered more economical and it is the most widely used methods in developing countries. Hence the potentials in the othealternative methods such as the resource recovery and recycling and their integration into waste management have been scarcelassessed. However, the ever growing challenges posed by the rapidly increasing quantities and compositions of solid wastes ideveloping countries led to the searching for alternative waste disposal methods. In this regard the paper presented an assessmenof the resource potentials of municipal solid waste materials arising from cities in developing countries as a strategy fosustainable solid waste management. Using published data on solid waste composition the paper has identified that there is higpotentials of composting in the solid waste stream from cities in developing countries. In conclusion, it recommended the recoverof organic waste material and papers for composting and the recycling of plastic, metals, textiles and others to explore their resource recovery potentials. This will largely reduce the ultimate quantities of solid waste for disposal and lower the operatincosts. This strategy will achieve sustainable waste management in developing countries. It is hoped that the paper has provided useful guide for wastes management policy decisions in developing countries

On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters

This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p

Min Max Generalization for Two-stage Deterministic Batch Mode Reinforcement Learning: Relaxation Schemes

We study the minmax optimization problem introduced in [22] for computing policies for batch mode reinforcement learning in a deterministic setting. First, we show that this problem is NP-hard. In the two-stage case, we provide two relaxation schemes. The first relaxation scheme works by dropping some constraints in order to obtain a problem that is solvable in polynomial time. The second relaxation scheme, based on a Lagrangian relaxation where all constraints are dualized, leads to a conic quadratic programming problem. We also theoretically prove and empirically illustrate that both relaxation schemes provide better results than those given in [22]

A Review of Norms and Normative Multiagent Systems

Norms and normative multiagent systems have become the subjects of interest for many researchers. Such interest is caused by the need for agents to exploit the norms in enhancing their performance in a community. The term norm is used to characterize the behaviours of community members. The concept of normative multiagent systems is used to facilitate collaboration and coordination among social groups of agents. Many researches have been conducted on norms that investigate the fundamental concepts, definitions, classification, and types of norms and normative multiagent systems including normative architectures and normative processes. However, very few researches have been found to comprehensively study and analyze the literature in advancing the current state of norms and normative multiagent systems. Consequently, this paper attempts to present the current state of research on norms and normative multiagent systems and propose a norm’s life cycle model based on the review of the literature. Subsequently, this paper highlights the significant areas for future work

Classificação de pacientes para adaptação de cadeira de rodas inteligente

Doutoramento em Engenharia InformáticaA importância e preocupação dedicadas à autonomia e independência das pessoas idosas e dos pacientes que sofrem de algum tipo de deficiência tem vindo a aumentar significativamente ao longo das últimas décadas. As cadeiras de rodas inteligentes (CRI) são tecnologias que podem ajudar este tipo de população a aumentar a sua autonomia, sendo atualmente uma área de investigação bastante ativa. Contudo, a adaptação das CRIs a pacientes específicos e a realização de experiências com utilizadores reais são assuntos de estudo ainda muito pouco aprofundados. A cadeira de rodas inteligente, desenvolvida no âmbito do Projeto IntellWheels, é controlada a alto nível utilizando uma interface multimodal flexível, recorrendo a comandos de voz, expressões faciais, movimentos de cabeça e através de joystick. Este trabalho teve como finalidade a adaptação automática da CRI atendendo às características dos potenciais utilizadores. Foi desenvolvida uma metodologia capaz de criar um modelo do utilizador. A investigação foi baseada num sistema de recolha de dados que permite obter e armazenar dados de voz, expressões faciais, movimentos de cabeça e do corpo dos pacientes. A utilização da CRI pode ser efetuada em diferentes situações em ambiente real e simulado e um jogo sério foi desenvolvido permitindo especificar um conjunto de tarefas a ser realizado pelos utilizadores. Os dados foram analisados recorrendo a métodos de extração de conhecimento, de modo a obter o modelo dos utilizadores. Usando os resultados obtidos pelo sistema de classificação, foi criada uma metodologia que permite selecionar a melhor interface e linguagem de comando da cadeira para cada utilizador. A avaliação para validação da abordagem foi realizada no âmbito do Projeto FCT/RIPD/ADA/109636/2009 - "IntellWheels - Intelligent Wheelchair with Flexible Multimodal Interface". As experiências envolveram um vasto conjunto de indivíduos que sofrem de diversos níveis de deficiência, em estreita colaboração com a Escola Superior de Tecnologia de Saúde do Porto e a Associação do Porto de Paralisia Cerebral. Os dados recolhidos através das experiências de navegação na CRI foram acompanhados por questionários preenchidos pelos utilizadores. Estes dados foram analisados estatisticamente, a fim de provar a eficácia e usabilidade na adequação da interface da CRI ao utilizador. Os resultados mostraram, em ambiente simulado, um valor de usabilidade do sistema de 67, baseado na opinião de uma amostra de pacientes que apresentam os graus IV e V (os mais severos) de Paralisia Cerebral. Foi também demonstrado estatisticamente que a interface atribuída automaticamente pela ferramenta tem uma avaliação superior à sugerida pelos técnicos de Terapia Ocupacional, mostrando a possibilidade de atribuir automaticamente uma linguagem de comando adaptada a cada utilizador. Experiências realizadas com distintos modos de controlo revelaram a preferência dos utilizadores por um controlo compartilhado com um nível de ajuda associado ao nível de constrangimento do paciente. Em conclusão, este trabalho demonstra que é possível adaptar automaticamente uma CRI ao utilizador com claros benefícios a nível de usabilidade e segurança.The importance and concern given to the autonomy and independence of elderly people and patients suffering from some kind of disability has been growing significantly in the last few decades. Intelligent wheelchairs (IW) are technologies that can increase the autonomy and independence of this kind of population and are nowadays a very active research area. However, the adaptations to users’ specificities and experiments with real users are topics that lack deeper studies. The intelligent wheelchair, developed in the context of the IntellWheels project, is controlled at a high-level through a flexible multimodal interface, using voice commands, facial expressions, head movements and joystick as its main input modalities. This work intended to develop a system enabling the automatic adaptation, to the user characteristics, of the previously developed intelligent wheelchair. A methodology was created enabling the creation of a user model. The research was based on the development of a data gathering system, enabling the collection and storage of data from voice commands, facial expressions, head and body movements from several patients with distinct disabilities such as Cerebral Palsy. The wheelchair can be used in different situations in real and simulated environments and a serious game was developed where different tasks may be performed by users. Data was analysed using knowledge discovery methods in order to create an automatic patient classification system. Based on the classification system, a methodology was developed enabling to select the best wheelchair interface and command language for each patient. Evaluation was performed in the context of Project FCT/RIPD/ADA/109636/ 2009 – “IntellWheels – Intelligent Wheelchair with Flexible Multimodal Interface”. Experiments were conducted, using a large set of patients suffering from severe physical constraints in close collaboration with Escola Superior de Tecnologia de Saúde do Porto and Associação do Porto de Paralisia Cerebral. The experiments using the intelligent wheelchair were followed by user questionnaires. The results were statistically analysed in order to prove the effectiveness and usability of the adaptation of the Intelligent Wheelchair multimodal interface to the user characteristics. The results obtained in a simulated environment showed a 67 score on the system usability scale based in the opinion of a sample of cerebral palsy patients with the most severe cases IV and V of the Gross Motor Function Scale. It was also statistically demonstrated that the data analysis system advised the use of an adapted interface with higher evaluation than the one suggested by the occupational therapists, showing the usefulness of defining a command language adapted to each user. Experiments conducted with distinct control modes revealed the users' preference for a shared control with an aid level taking into account the level of constraint of the patient. In conclusion, this work demonstrates that it is possible to adapt an intelligent wheelchair to the user with clear usability and safety benefits

Safe Reinforcement Learning Using Formally Verified Abstract Policies

Reinforcement learning (RL) is an artificial intelligence technique for finding optimal solutions for sequential decision-making problems modelled as Markov decision processes (MDPs). Objectives are represented as numerical rewards in the model where positive values represent achievements and negative values represent failures. An autonomous agent explores the model to locate rewards with the goal to learn behaviour which will cumulate the largest reward possible. Despite RL successes in applications ranging from robotics and planning systems to sensing, it has so far had little appeal in mission- and safety-critical systems where unpredictable agent actions could lead to mission failure, risks to humans, itself or other systems, or violations of legal requirements. This is due to the difficulty of encoding non-trivial requirements of agent behaviour through rewards alone. This thesis introduces assured reinforcement learning (ARL), a safe RL approach that restricts agent actions, during and after learning. This restriction is based on formally verified policies synthesised for a high-level, abstract MDP that models the safety-relevant aspects of the RL problem. The resulting actions form overall solutions whose properties satisfy strict safety and optimality requirements. Next, ARL with knowledge revision is introduced, allowing ARL to still be used if the initial knowledge for generating action constraints proves to be incorrect. Additionally, two case studies are introduced to test the efficacy of ARL: the first is an adaptation of the benchmark flag collection navigation task and the second is an assisted-living planning system. Finally, an architecture for runtime ARL is proposed to allow ARL to be utilised in real-time systems. ARL is empirically evaluated and is shown to successfully satisfy strict safety and optimality requirements and, furthermore, with knowledge revision and action reuse, it can be successfully applied in environments where initial information may prove incomplete or incorrect