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

Distributed coordination in unstructured intelligent agent societies

Current research on multi-agent coordination and distributed problem solving is still not robust or scalable enough to build large real-world collaborative agent societies because it relies on either centralised components with full knowledge of the domain or pre-defined social structures. Our approach allows overcoming these limitations by using a generic coordination framework for distributed problem solving on totally unstructured environments that enables each agent to decompose problems into sub-problems, identify those which it can solve and search for other agents to delegate the sub-problems for which it does not have the necessary knowledge or resources. Regarding the problem decomposition process, we have developed two distributed versions of the Graphplan planning algorithm. To allow an agent to discover other agents with the necessary skills for dealing with unsolved sub-problems, we have created two peer-to-peer search algorithms that build and maintain a semantic overlay network that connects agents relying on dependency relationships, which improves future searches. Our approach was evaluated using two different scenarios, which allowed us to conclude that it is efficient, scalable and robust, allowing the coordinated distributed solving of complex problems in unstructured environments without the unacceptable assumptions of alternative approaches developed thus far.As abordagens actuais de coordenação multi-agente e resolução distribuída de problemas não são suficientemente robustas ou escaláveis para criar sociedades de agentes colaborativos uma vez que assentam ou em componentes centralizados com total conhecimento do domínio ou em estruturas sociais pré-definidas. A nossa abordagem permite superar estas limitações através da utilização de um algoritmo genérico de coordenação de resolução distribuída de problemas em ambientes totalmente não estruturados, o qual permite a cada agente decompor problemas em sub-problemas, identificar aqueles que consegue resolver e procurar outros agentes a quem delegar os subproblemas para os quais não tem conhecimento suficiente. Para a decomposição de problemas, criámos duas versões distribuídas do algoritmo de planeamento Graphplan. Para procurar os agentes com as capacidades necessárias à resolução das partes não resolvidas do problema, criámos dois algoritmos de procura que constroem e mantêm uma camada de rede semântica que relaciona agentes dependentes com o fim de facilitar as procuras. A nossa abordagem foi avaliada em dois cenários diferentes, o que nos permitiu concluir que ´e uma abordagem eficiente, escalável e robusta, possibilitando a resolução distribuída e coordenada de problemas complexos em ambientes não estruturados sem os pressupostos inaceitáveis em que assentava o trabalho feito até agora

Applications of AI planning in genome rearrangement and in multi-robot systems

In AI planning the aim is to plan the actions of an agent to achieve the given goals from a given initial state. We use AI planning to solve two challenging problems: the genome rearrangement problem in computational biology and the decoupled planning problem in multi-robot systems. Motivated by the reconstruction of phylogenies, the genome rearrangement problem seeks to find the minimum number of rearrangement events (i.e., genome-wide mutations) between two given genomes. We introduce a novel method (called GENOMEPLAN) to solve this problem for single chromosome circular genomes with unequal gene content and/or duplicate genes, by formulating the pairwise comparison of entire genomes as an AI planning problem and using the AI planner TLPlan to compute solutions. The idea is to plan genome rearrangement events to transform one genome to the other. To improve computational efficiency, GENOMEPLAN embeds several heuristics in the descriptions of these events. To better understand the evolutionary history of species and to find more plausible solutions, GENOMEPLAN allows assigning costs and priorities to rearrangement events. The applicability of GENOMEPLAN is shown by some experiments on real data sets as well as randomly generated instances. In multi-robot systems, multiple teams of heterogeneous robots work in separate workspaces towards different goals. The teams are allowed to lend robots to one another. The goal is to find an overall plan of minimum length where each team completes its assigned task. We introduce an intelligent algorithm to solve this problem. The idea is, on the one hand, to allow each team to autonomously find its own plan and, on the other hand, to allow a central agent to communicate with the representatives of the teams to find an optimal decoupled plan. We prove the soundness and completeness of our decoupled planning algorithm, and analyze its computational complexity. We show the applicability of our approach on an intelligent factory scenario, using the action description language C+ for representing the domain and the causal reasoner CCALC for reasoning about the domain