Social Continual Planning in Open Multiagent Systems: a First Study

Abstract. We describe a Multiagent Planning approach, named Social Continual Planning, that tackles open scenarios, where agents can join and leave the system dynamically. The planning task is not defined from a global point of view, setting a global objective, but we allow each agent to pursue its own subset of goals. We take a social perspective where, although each agent has its own planning task and planning algorithm, it needs to get engaged with others for accomplishing its own goals. Cooperation is not forced but, thanks to the abstraction of social commitment, stems from the needs of the agents

Mapeamento de vulnerabilidades decorrentes dos impactes dos sistemas de transportes rodoviários

O transporte rodoviário individual tem cada vez mais relevância, principalmente em cidades de média dimensão como Aveiro. Tal relevância deve-se parcialmente, à complexidade em implementar um sistema de transportes públicos altamente eficiente e atrativo devido à dispersão populacional dos subúrbios limítrofes ao núcleo urbano. Com o mapeamento foi possível efetuar um inventário organizado espacialmente dos vários impactes considerados, tendo sido encontrados alguns hotspots como a Av. 5 de Outubro, com emissões de CO2 e NOx 90% e 114% superiores, respetivamente, ao valor médio verificado. Através do mapeamento dos acidentes entre veículos e utentes vulneráveis foram encontrados alguns pontos negros. Conclui-se que um inventário de impactes pode ser uma ferramenta útil na identificação de hotspots

Mapeamento de vulnerabilidades decorrentes dos impactes dos sistemas de transportes rodoviários

A relevância do transporte rodoviário no modelo de sociedade atual acarreta vários impactes do foro ambiental e social, como emissões de poluentes atmosféricos e acidentes rodoviários. Esta dissertação propõe a criação de uma base de dados, num sistema de informação geográfica que organiza espacialmente a informação, para englobar várias vulnerabilidades recorrentes dos impactes do sistema de transportes, com incidência no transporte rodoviário, para identificação de pontos críticos e posterior utilização em algoritmos de otimização de rota. Os impactes considerados foram as emissões de CO2, NOx e NMVOC, o nível de ruído e os acidentes rodoviários entre veículos e utentes vulneráveis. Foi ainda contabilizado os custos marginais das externalidades associados aos impactes considerados tendo cada segmento da rede rodoviária em estudo associada informação sobre estes impactes. Com recurso ao mapeamento dos impactes considerados foi possível identificar vários pontos críticos de emissões e de sinistralidade rodoviária, tendo sido também testados dois problemas de otimização de rota, onde foi estudada a implementação de um fator associado à exposição da população local aos impactes e com o congestionamento, realçando a importância de ter estes dois fatores em conta numa futura plataforma de otimização de tráfego. Ao utilizar um inventário de impactes organizado espacialmente num software de sistemas de informação geográfica é possível identificar zonas especialmente vulneráveis aos impactes, com algumas vias a terem um fator de emissão de CO2 90% superiores ao valor médio verificado, assim como otimizar a rota tendo em conta fatores como as emissões totais e o custo associado. Foi testado um fator de exposição às populações que fez aumentar o custo em cerca de 5%. No problema de otimização de rota onde foi testado a influência do congestionamento, foi possível verificar que as condições de tráfego têm o efeito de alterar os resultados finais. Foram testados vários tempos de viagem associados a uma rota mais curta (cerca de 700m em relação a uma rota alternativa) tendo-se verificado no caso de um veículo a gasolina, que a partir de um tempo de espera de cerca de 4 minutos, é compensatório do ponto de vista de redução de custos ambientais optar pela rota alternativa com percurso mais longo. Como principal conclusão salienta-se a eficácia da base de dados em inventariar os impactes associados à rede rodoviária em estudo, tendo sido identificados alguns hotspots de impactes. Também foi possível verificar que as condições de tráfego e a exposição das populações são fatores importantes que devem fazer parte de uma futura plataforma de otimização de tráfego.The relevance of road transportation in the current model society involves various environmental and social impacts such as pollutant emissions and road accidents. The main objective of this Dissertation is the creation of a database based on geographic information systems, in order to organize spatially several road transportation impacts, for identification of vulnerabilities hotspots and later use in route optimization algorithms. The impacts considered were CO2, NOx and NMVOC emissions, traffic noise levels and road crashes between vehicles and vulnerable users (pedestrians and cyclists), as well as social costs associated with the considered impacts. Each segment of road network under study contains information on these impacts in order to use the database in route optimization algorithms. Using the mapping of the considered impacts, emissions and road accidents hotspots were identified. Two route optimization problems were conceived where other factors such as the population exposure to the impacts and traffic congestion were considered, emphasizing the importance of taking these two factors into account in a future traffic optimization platform. By using a spatially organized inventory of impacts in a geographical information system, zones especially vulnerable to impacts were identified, with some road segments having a CO2 factor 90% higher than the average, as well as optimizing the route taking in account factors like the total emissions and social costs. A population exposure factor was tested, which increased the social costs by about 5%. In the route optimization problem where the influence of congestion was tested, it was verified that the traffic conditions may affect the results. In a route 700m shorter than the other, were tested numerous travel times to analyze from which travel time is it worth to opt for the alternative, but longer route. For a petrol car it is worth to choose the alternative route for a travel time higher than 4 minutes in the shorter route. As conclusions, one can state that a database of transportation impacts can be used to identify vulnerabilities hotspots. It was concluded that traffic conditions and population exposure to the impacts should be part of a future traffic optimization platform.Apoio financeiro do projeto e CENTRO-01-0145-FEDER-022083 Este trabalho insere-se no âmbito do projeto @CRUiSE (PTDC/EMS-TRA/0383/2014), financiado no âmbito do Projeto 9471 – Reforçar a Investigação, o Desenvolvimento Tecnológico e a Inovação (Projeto 9471 – RIDTI) e comparticipado pelo Fundo Comunitário Europeu FEDER, e no âmbito do Projeto Estratégico UID-EMS-00481-2013.Mestrado em Sistemas Energéticos Sustentávei

Beyond the Frontiers of Timeline-based Planning

Any agent, either biological or artificial, understands how to behave in its environment according to its prior knowledge and to its prior experience. The process of deciding which actions to undertake and how to perform them so as to achieve some desired objective is called deliberation. In particular, planning is an abstract and explicit deliberation process that chooses and organizes actions, by anticipating their expected outcomes, with the aim to achieve, as best as possible, some pre-stated objectives called goals. Among the most widespread approaches to automated planning, the classical approach broadly pursues to the following definition of planning: starting from a description of the initial state of the world, a description of the desired goals, and a description of a set of possible actions, the planning problem consists in synthesizing a plan, i.e., a sequence of actions, that is guaranteed, when applied to the initial state, to generate a state, called a goal state, which contains the desired goals. In order to cope with computational complexity, however, the classical approach to planning introduces some restrictive assumptions. Among them, for example, there is no explicit model of time and concurrency is treated only roughly. Additionally, goals are specified as a set of goal states, therefore, objectives such as states to be avoided and constraints on state trajectories or utility functions are not handled. In order to relax these restrictions, some alternative approaches have been proposed over the years. The timeline-based approach to planning, in particular, represents an effective alternative to classical planning for complex domains requiring the use of both temporal reasoning and scheduling features. This thesis focuses on timeline-based planning, aiming at solving some efficiency issues which inevitably raise as a consequence of the drop out of these restrictions. Regardless of the followed approach, indeed, it turns out that automated planning is a rather complex task from a computational point of view. Furthermore, not all of the approaches proposed in literature can rely on effective heuristics for efficiently tackling the search. This is particularly true in the case of the more recent and hence less investigated timeline-based formulation. Most of the timeline-based planners, in particular, have usually neglected the advantages triggered in classical planning from the use of Graphplan and/or modern heuristic search, namely the capability of reasoning on the whole domain model. This thesis aims at reducing the performance gap between the classical approach at planning and the timeline-based one. Specifically, the overall goal is to improve the efficiency of timeline-based reasoners taking inspiration from techniques applied in more classical approaches to planning. The main contributions of this thesis, therefore, are a) a new formalism for timeline-based planning which overcomes some limitations of the existing ones; b) a set of heuristics, inspired by the classical approach, that improve the performance of the timeline-based approach to planning; c) the introduction of sophisticated techniques like the non-chronological backtracking and the no-good learning, commonly used in other fields such as Constraint Processing, into the search process;d) the reorganization of the existing solver architectures, of a new solver called ORATIO, that allows to push the reasoning process beyond the sole automated planning, winking at emerging fields like, for example, Explainable AI and e) the introduction of a new language for expressing timeline-based planning problems called RIDDLE