Planning & Scheduling Applications in Urban Traffic Management

Local authorities that manage traffic-related issues in urban areas have to optimise the use of available resources, in order to minimise congestion and delays. In this context, Automated Planning and Scheduling can be fruitfully exploited, in order to provide dynamic plans that help managing the urban road network. In this paper we provide a review of existing planning and scheduling approaches that have been designed for dealing with different aspects of traffic management, with the aim of gaining insights on the limits of current applications, and highlighting the open challenges

Automated Planning for Urban Traffic Control: Strategic Vehicle Routing to Respect Air Quality Limitations

The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. These trends are occurring in the context of concerns around environmental issues of poor air quality and transport related carbon dioxide emissions. One out of several ways to help meet these challenges is in the intelligent routing of road traffic through congested urban areas. Our goal is to show the feasibility of using automated planning to perform this routing, taking into account a knowledge of vehicle types, vehicle emissions, route maps, air quality zones, etc. Specifically focusing on air quality concerns, in this paper we investigate the problem where the goals are to minimise overall vehicle delay while utilising network capacity fully, and respecting air quality limits. We introduce an automated planning approach for the routing of traffic to address these areas. The approach has been evaluated on micro-simulation models that use real-world data supplied by our industrial partner. Results show the feasibility of using AI planning technology to deliver efficient routes for vehicles that avoid the breaking of air quality limits, and that balance traffic flow through the network

Towards full-scale autonomy for multi-vehicle systems planning and acting in extreme environments

Currently, robotic technology offers flexible platforms for addressing many challenging problems that arise in extreme environments. These problems’ nature enhances the use of heterogeneous multi-vehicle systems which can coordinate and collaborate to achieve a common set of goals. While such applications have previously been explored in limited contexts, long-term deployments in such settings often require an advanced level of autonomy to maintain operability. The success of planning and acting approaches for multi-robot systems are conditioned by including reasoning regarding temporal, resource and knowledge requirements, and world dynamics. Automated planning provides the tools to enable intelligent behaviours in robotic systems. However, whilst many planning approaches and plan execution techniques have been proposed, these solutions highlight an inability to consistently build and execute high-quality plans. Motivated by these challenges, this thesis presents developments advancing state-of-the-art temporal planning and acting to address multi-robot problems. We propose a set of advanced techniques, methods and tools to build a high-level temporal planning and execution system that can devise, execute and monitor plans suitable for long-term missions in extreme environments. We introduce a new task allocation strategy, called HRTA, that optimises the task distribution amongst the heterogeneous fleet, relaxes the planning problem and boosts the plan search. We implement the TraCE planner that enforces contingent planning considering propositional temporal and numeric constraints to deal with partial observability about the initial state. Our developments regarding robust plan execution and mission adaptability include the HLMA, which efficiently optimises the task allocation and refines the planning model considering the experience from robots’ previous mission executions. We introduce the SEA failure solver that, combined with online planning, overcomes unexpected situations during mission execution, deals with joint goals implementation, and enhances mission operability in long-term deployments. Finally, we demonstrate the efficiency of our approaches with a series of experiments using a new set of real-world planning domains.Engineering and Physical Sciences Research Council (EPSRC) grant EP/R026173/

Inference and Learning with Planning Models

[ES] Inferencia y aprendizaje son los actos de razonar sobre evidencia recogida con el fin de alcanzar conclusiones lógicas sobre el proceso que la originó. En el contexto de un modelo de espacio de estados, inferencia y aprendizaje se refieren normalmente a explicar el comportamiento pasado de un agente, predecir sus acciones futuras, o identificar su modelo. En esta tesis, presentamos un marco para inferencia y aprendizaje en el modelo de espacio de estados subyacente al modelo de planificación clásica, y formulamos una paleta de problemas de inferencia y aprendizaje bajo este paraguas unificador. También desarrollamos métodos efectivos basados en planificación que nos permiten resolver estos problemas utilizando algoritmos de planificación genéricos del estado del arte. Mostraremos que un gran número de problemas de inferencia y aprendizaje claves que han sido tratados como desconectados se pueden formular de forma cohesiva y resolver siguiendo procedimientos homogéneos usando nuestro marco. Además, nuestro trabajo abre las puertas a nuevas aplicaciones para tecnología de planificación ya que resalta las características que hacen que el modelo de espacio de estados de planificación clásica sea diferente a los demás modelos.[CA] Inferència i aprenentatge són els actes de raonar sobre evidència arreplegada a fi d'aconseguir conclusions lògiques sobre el procés que la va originar. En el context d'un model d'espai d'estats, inferència i aprenentatge es referixen normalment a explicar el comportament passat d'un agent, predir les seues accions futures, o identificar el seu model. En esta tesi, presentem un marc per a inferència i aprenentatge en el model d'espai d'estats subjacent al model de planificació clàssica, i formulem una paleta de problemes d'inferència i aprenentatge davall este paraigua unificador. També desenrotllem mètodes efectius basats en planificació que ens permeten resoldre estos problemes utilitzant algoritmes de planificació genèrics de l'estat de l'art. Mostrarem que un gran nombre de problemes d'inferència i aprenentatge claus que han sigut tractats com desconnectats es poden formular de forma cohesiva i resoldre seguint procediments homogenis usant el nostre marc. A més, el nostre treball obri les portes a noves aplicacions per a tecnologia de planificació ja que ressalta les característiques que fan que el model d'espai d'estats de planificació clàssica siga diferent dels altres models.[EN] Inference and learning are the acts of reasoning about some collected evidence in order to reach a logical conclusion regarding the process that originated it. In the context of a state-space model, inference and learning are usually concerned with explaining an agent's past behaviour, predicting its future actions or identifying its model. In this thesis, we present a framework for inference and learning in the state-space model underlying the classical planning model, and formulate a palette of inference and learning problems under this unifying umbrella. We also develop effective planning-based approaches to solve these problems using off-the-shelf, state-of-the-art planning algorithms. We will show that several core inference and learning problems that previous research has treated as disconnected can be formulated in a cohesive way and solved following homogeneous procedures using the proposed framework. Further, our work opens the way for new applications of planning technology as it highlights the features that make the state-space model of classical planning different from other models.The work developed in this doctoral thesis has been possible thanks to the FPU16/03184 fellowship that I have enjoyed for the duration of my PhD studies. I have also been supported by my advisors’ grants TIN2017-88476-C2-1-R, TIN2014-55637-C2-2-R-AR, and RYC-2015-18009.Aineto García, D. (2022). Inference and Learning with Planning Models [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/18535

Task scheduling and merging in space and time

Every day, robots are being deployed in more challenging environments, where they are required to perform complex tasks. In order to achieve these tasks, robots rely on intelligent deliberation algorithms. In this thesis, we study two deliberation approaches – task scheduling and task planning. We extend these approaches in order to not only deal with temporal and spatial constraints imposed by the environment, but also exploit them to be more efficient than the state-of-the-art approaches. Our first main contribution is a scheduler that exploits a heuristic based on Allen’s interval algebra to prune the search space to be traversed by a mixed integer program. We empirically show that the proposed scheduler outperforms the state of the art by at least one order of magnitude. Furthermore, the scheduler has been deployed on several mobile robots in long-term autonomy scenarios. Our second main contribution is the POPMERX algorithm, which is based on merging of partially ordered temporal plans. POPMERX first reasons with the spatial and temporal structure of separately generated plans. Then, it merges these plans into a single final plan, while optimising the makespan of the merged plan. We empirically show that POPMERX produces better plans that the-state-ofthe- art planners on temporal domains with time windows

A Synthesis of Automated Planning and Model Predictive Control Techniques and its Use in Solving Urban Traffic Control Problem

Most desired applications for planning and scheduling typically have the characteristics of a continuous changing world. Unfortunately, traditional classical planning does not possess this characteristic. This drawback is because most real-world situations involve quantities and numeric values, which cannot be adequately represented in classical planning. Continuous planning in domains that are represented with rich notations is still a great challenge for AI. For instance, changes occurring due to fuel consumption, continuous movement, or environmental conditions may not be adequately modelled through instantaneous or even durative actions; rather these require modelling as continuously changing processes. The development of planning tools that can reason with domains involving continuous and complex numeric fluents would facilitate the integration of automated planning in the design and development of complex application models to solve real world problems. Traditional urban traffic control (UTC) approaches are still not very efficient during unforeseen situations such as road incidents when changes in traffic are requested in a short time interval. For such anomalies, we need systems that can plan and act effectively in order to restore an unexpected road traffic situation into a normal order. In the quest to improve reasoning with continuous process within the UTC domain, we investigate the role of Model Predictive Control (MPC) approach to planning in the presence of mixed discrete and continuous state variables within a UTC problem. We explore this control approach and show how it can be embedded into existing, modern AI Planning technology. This approach preserves the many advantages of the AI Planning approach, to do with domain independence through declarative modelling, and explicit reasoning while leveraging the capability of MPC to deal with continuous processes. We evaluate the possibility of reasoning with the knowledge of UTC structures to optimise traffic flow in situations where a given road within a network of roads becomes unavailable due to unexpected situations such as road accidents. We specify how to augment the standard AI planning engine with the incorporation of MPC techniques into the central reasoning process of a continuous domain. This approach effectively utilises the strengths of search-based and model-simulation-based methods. We create a representation that can be used to capture declaratively, the definitions of processes, actions, events, resources resumption and the structure of the environment in a UTC scenario. This representation is founded on world states modelled by mixed discrete and continuous state variables. We create a planner with a hybrid algorithm, called UTCPLAN that combines both AI planning and MPC approach to reason with traffic network and control traffic signal at junctions within the network. The experimental objective of minimising the number of vehicles in a queue is implemented to validate the applicability and effectiveness of the algorithm. We present an experimental evaluation showing that our approach can provide UTC plans in a reasonable time. The result also shows that the UTCPLAN approach can perform well in dealing with heavy traffic congestion problems, which might result from heavy traffic flow during rush hours

Discovering and Utilising Expert Knowledge from Security Event Logs

Security assessment and configuration is a methodology of protecting computer systems from malicious entities. It is a continuous process and heavily dependent on human experts, which are widely attributed to being in short supply. This can result in a system being left insecure because of the lack of easily accessible experience and specialist resources. While performing security tasks, human experts often revert to a system's event logs to determine status of security, such as failures, configuration modifications, system operations etc. However, finding and exploiting knowledge from event logs is a challenging and time-consuming task for non-experts. Hence, there is a strong need to provide mechanisms to make the process easier for security experts, as well as providing tools for those with significantly less security expertise. Doing so automatically allows for persistent and methodical testing without an excessive amount of manual time and effort, and makes computer security more accessible to on-experts. In this thesis, we present a novel technique to process security event logs of a system that have been evaluated and configured by a security expert, extract key domain knowledge indicative of human decision making, and automatically apply acquired knowledge to previously unseen systems by non-experts to recommend security improvements. The proposed solution utilises association and causal rule mining techniques to automatically discover relationships in the event log entries. The relationships are in the form of cause and effect rules that define security-related patterns. These rules and other relevant information are encoded into a PDDL-based domain action model. The domain model and problem instance generated from any vulnerable system can then be used to produce a plan-of-action by employing a state-of-the-art automated planning algorithm. The plan can be exploited by non-professionals to identify the security issues and make improvements. Empirical analysis is subsequently performed on 21 live, real world event log datasets, where the acquired domain model and identified plans are closely examined. The solution's accuracy lies between 73% - 92% and gained a significant performance boost as compared to the manual approach of identifying event relationships. The research presented in this thesis is an automation of extracting knowledge from event data steams. The previous research and current industry practices suggest that this knowledge elicitation is performed by human experts. As evident from the empirical analysis, we present a promising line of work that has the capacity to be utilised in commercial settings. This would reduce (or even eliminate) the dire and immediate need for human resources along with contributing towards financial savings

Online plan modification in uncertain resource-constrained environments

This thesis presents a novel approach to planning under uncertainty in resource constrained environments. Such environments feature in many real-world applications, including planetary rover and autonomous underwater vehicle (AUV) missions. Our focus is on long-duration AUV missions, in which a vehicle spends months at sea, with little or no opportunity for intervention. As the risk to the vehicle and cost of deployment are significant, it is important to fully utilise each mission, maximising data return without compromising vehicle safety. Planning within this domain is challenging because significant resource usage uncertainty prevents computation of an optimal strategy in advance. We describe our novel method for online plan modification and execution monitoring, which augments an existing plan with pre-computed plan fragments in response to observed resource availability. Our modification algorithm uses causal structure to interleave actions, creating solutions without introducing significant computational cost. Our system monitors resource availability, reasoning about the probability of successfully completing the goals. We show that when the probability of completing the mission decreases, by removing low-priority goals our system reduces the risk to the vehicle, increasing mission success rate. Conversely, when resource availability allows, by including additional goals our system increases reward without adversely affecting success rate