The Experts below are selected from a list of 4128 Experts worldwide ranked by ideXlab platform

Mauro Vallati - One of the best experts on this subject based on the ideXlab platform.

  • ICAPS - Embedding Automated Planning within Urban Traffic Management Operations
    2020
    Co-Authors: Thomas Leo Mccluskey, Mauro Vallati
    Abstract:

    This paper is an experience report on the results of an industry-led collaborative project aimed at automating the control of traffic flow within a large city centre. A major focus of the automation was to deal with abnormal or unexpected events such as roadworks, road closures or excessive demand, resulting in periods of saturation of the network within some region of the city. We describe the resulting system which works by sourcing and semantically enriching urban traffic data, and uses the derived knowledge as input to an Automated Planning component to generate light signal control strategies in real time. This paper reports on the development surrounding the Planning component, and in particular the engineering, configuration and validation issues that arose in the application. It discusses a range of lessons learned from the experience of deploying Automated Planning in the road transport area, under the direction of transport operators and technology developers.

  • SGAI Conf. - A General Approach to Exploit Model Predictive Control for Guiding Automated Planning Search in Hybrid Domains
    Lecture Notes in Computer Science, 2019
    Co-Authors: Faizan Bhatti, Diane E. Kitchin, Mauro Vallati
    Abstract:

    Automated Planning techniques are increasingly exploited in real-world applications, thanks to their flexibility and robustness. Hybrid domains, those that require to reason both with discrete and continuous aspects, are particularly challenging to handle with existing Planning approaches due to their complex dynamics. In this paper we present a general approach that allows to combine the strengths of Automated Planning and control systems to support reasoning in hybrid domains. In particular, we propose an architecture to integrate Model Predictive Control (MPC) techniques from the field of control systems into an Automated planner, to guide the effective exploration of the search space.

  • K-CAP - Engineering Knowledge for Automated Planning: Towards a Notion of Quality
    Proceedings of the Knowledge Capture Conference on - K-CAP 2017, 2017
    Co-Authors: Thomas Leo Mccluskey, Tiago Stegun Vaquero, Mauro Vallati
    Abstract:

    Automated Planning is a prominent Artificial Intelligence challenge, as well as being a common capability requirement for intelligent autonomous agents. A critical aspect of what is called domain-independent Planning, is the application knowledge that must be added to the planner to create a complete Planning application. This is made explicit in (i) a domain model, which is a formal representation of the persistent domain knowledge, and (ii) an associated problem instance, containing the details of the particular problem to be solved. Both these components are used by Automated Planning engines for reasoning, in order to synthesize a solution plan. Formulating knowledge for use in Planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers significantly influence the quality of the resulting Planning application. On top of that, a notion of quality of the knowledge captured within a domain model is missing; it is therefore hard to provide useful guidelines to knowledge engineers. This paper raises some issues relating to the engineering of application knowledge for Automated Planning, focussing on the domain model. It uses the idea of a domain model as a formal specification of a domain, and considers what it means to measure the quality of such a specification. To do this it proposes definitions of the attributes of a domain model and its encoding language, which are needed by the Automated Planning community in order to improve tools for supporting the engineering of Planning knowledge, and to advance toward a shared and inclusive definition of quality of domain models.

  • IJCAI - Automated Planning for Urban Traffic Management
    Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017
    Co-Authors: Thomas Leo Mccluskey, Mauro Vallati, Santiago Franco
    Abstract:

    The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. Optimising the exploitation of urban road network, while attempting to minimise the effects of traffic emissions, is a great challenge. SimplyfAI was a UK research council grant funded project which was aimed towards solving air quality problems caused by road traffic emissions. Large cities such as Manchester struggle to meet air quality limits as the range of available traffic management devices is limited. In the study, we investigated the application of linked data to enrich environmental and traffic data feeds, and we used this with Automated Planning tools to enable traffic to be managed at a region level. The management will have the aim of avoiding air pollution problems before they occur. This demo focuses on the Planning component, and in particular the engineering and validation aspects, that were pivotal for the success of the project

  • CDC - On the exploitation of Automated Planning for efficient decision making in road traffic accident management
    2016 IEEE 55th Conference on Decision and Control (CDC), 2016
    Co-Authors: Lukáš Chrpa, Mauro Vallati
    Abstract:

    Automated Planning can be fruitfully exploited as a Decision Support toolkit that, given a specification of available actions (elementary decisions to be taken), an initial situation and goals to be achieved, generates a plan that represents a (partially ordered) sequence of such elementary decisions that once performed the required goals are achieved. Road Traffic Accident Management is a life-critical task that deals with effective Planning of emergency response when accidents occur, in order to mitigate negative effects, especially saving human lives that might be in imminent danger. In this paper, we exploit Automated Planning in the Road Traffic Accident Management domain. We specifically focus on providing necessary treatment for victims injured during accidents. This involves coordination of medical teams responsible for providing medical treatment to the victims and fire brigades that are required to release victims trapped in damaged vehicles. An empirical analysis, based in the region ofWest Yorkshire (UK) with a number of real accidents recently occurred there, shows the suitability of the proposed Automated Planning approach to be used in time-critical conditions, and confirms the effectiveness of the generated plans. We also demonstrated its usefulness as a tool for evaluating the impact of additional resources, in order to provide guidance for future investments.

Lukáš Chrpa - One of the best experts on this subject based on the ideXlab platform.

  • CDC - On the exploitation of Automated Planning for efficient decision making in road traffic accident management
    2016 IEEE 55th Conference on Decision and Control (CDC), 2016
    Co-Authors: Lukáš Chrpa, Mauro Vallati
    Abstract:

    Automated Planning can be fruitfully exploited as a Decision Support toolkit that, given a specification of available actions (elementary decisions to be taken), an initial situation and goals to be achieved, generates a plan that represents a (partially ordered) sequence of such elementary decisions that once performed the required goals are achieved. Road Traffic Accident Management is a life-critical task that deals with effective Planning of emergency response when accidents occur, in order to mitigate negative effects, especially saving human lives that might be in imminent danger. In this paper, we exploit Automated Planning in the Road Traffic Accident Management domain. We specifically focus on providing necessary treatment for victims injured during accidents. This involves coordination of medical teams responsible for providing medical treatment to the victims and fire brigades that are required to release victims trapped in damaged vehicles. An empirical analysis, based in the region ofWest Yorkshire (UK) with a number of real accidents recently occurred there, shows the suitability of the proposed Automated Planning approach to be used in time-critical conditions, and confirms the effectiveness of the generated plans. We also demonstrated its usefulness as a tool for evaluating the impact of additional resources, in order to provide guidance for future investments.

  • Automated Planning for Urban Traffic Control: Strategic Vehicle Routing to Respect Air Quality Limitations
    Intelligenza Artificiale, 2016
    Co-Authors: Lukáš Chrpa, Thomas Leo Mccluskey, Daniele Magazzeni, Keith Mccabe, Mauro Vallati
    Abstract:

    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.

  • On the exploitation of Automated Planning for efficient decision making in road traffic accident management
    2016 IEEE 55th Conference on Decision and Control (CDC), 2016
    Co-Authors: Lukáš Chrpa, Mauro Vallati
    Abstract:

    Automated Planning can be fruitfully exploited as a Decision Support toolkit that, given a specification of available actions (elementary decisions to be taken), an initial situation and goals to be achieved, generates a plan that represents a (partially ordered) sequence of such elementary decisions that once performed the required goals are achieved. Road Traffic Accident Management is a life-critical task that deals with effective Planning of emergency response when accidents occur, in order to mitigate negative effects, especially saving human lives that might be in imminent danger. In this paper, we exploit Automated Planning in the Road Traffic Accident Management domain. We specifically focus on providing necessary treatment for victims injured during accidents. This involves coordination of medical teams responsible for providing medical treatment to the victims and fire brigades that are required to release victims trapped in damaged vehicles. An empirical analysis, based in the region ofWest Yorkshire (UK) with a number of real accidents recently occurred there, shows the suitability of the proposed Automated Planning approach to be used in time-critical conditions, and confirms the effectiveness of the generated plans. We also demonstrated its usefulness as a tool for evaluating the impact of additional resources, in order to provide guidance for future investments.

  • Towards Automated Planning Domain Models Generation
    2013
    Co-Authors: Mauro Vallati, Lukáš Chrpa, Federico Cerutti
    Abstract:

    It is a common practice in Automated Planning to evaluate algorithms on existing benchmark domains. The number of domain models is limited, since they are encoding simplified versions of real-world domains and the generation of a new Planning domain is a complex task. The limited number of domain models does not allow to have a complete overview of the performances of Automated Planning engines. It would then be useful to have a generator of Planning domain models for improving the evaluation of Planning algorithms. In this paper we introduce the requirements that an automatic generator of random domain models should fulfill, and we discuss the related works and the main issues that a domain models generator will have to face.

David Garlan - One of the best experts on this subject based on the ideXlab platform.

  • ASE - Automated Planning for software architecture evolution
    2013 28th IEEE ACM International Conference on Automated Software Engineering (ASE), 2013
    Co-Authors: Jeffrey M. Barnes, Ashutosh Pandey, David Garlan
    Abstract:

    In previous research, we have developed a theoretical framework to help software architects make better decisions when Planning software evolution. Our approach is based on representation and analysis of candidate evolution paths--sequences of transitional architectures leading from the current system to a desired target architecture. One problem with this kind of approach is that it imposes a heavy burden on the software architect, who must explicitly define and model these candidate paths. In this paper, we show how Automated Planning techniques can be used to support automatic generation of evolution paths, relieving this burden on the architect. We illustrate our approach by applying it to a data migration scenario, showing how this architecture evolution problem can be translated into a Planning problem and solved using existing Automated Planning tools.

  • Automated Planning for software architecture evolution
    2013 28th IEEE ACM International Conference on Automated Software Engineering (ASE), 2013
    Co-Authors: Jeffrey M. Barnes, Ashutosh Pandey, David Garlan
    Abstract:

    In previous research, we have developed a theoretical framework to help software architects make better decisions when Planning software evolution. Our approach is based on representation and analysis of candidate evolution paths-sequences of transitional architectures leading from the current system to a desired target architecture. One problem with this kind of approach is that it imposes a heavy burden on the software architect, who must explicitly define and model these candidate paths. In this paper, we show how Automated Planning techniques can be used to support automatic generation of evolution paths, relieving this burden on the architect. We illustrate our approach by applying it to a data migration scenario, showing how this architecture evolution problem can be translated into a Planning problem and solved using existing Automated Planning tools.

Rune Moller Jensen - One of the best experts on this subject based on the ideXlab platform.

  • Automated Planning for liner shipping fleet repositioning
    International Conference on Automated Planning and Scheduling, 2012
    Co-Authors: Kevin Tierney, Amanda Coles, Andrew Coles, Christian Kroer, Adam M Britt, Rune Moller Jensen
    Abstract:

    The Liner Shipping Fleet Repositioning Problem (LSFRP) poses a large financial burden on liner shipping firms. During repositioning, vessels are moved between services in a liner shipping network. The LSFRP is characterized by chains of interacting activities, many of which have costs that are a function of their duration; for example, sailing slowly between two ports is cheaper than sailing quickly. Despite its great industrial importance, the LSFRP has received little attention in the literature. We show how the LSFRP can be solved sub-optimally using the planner POPF and optimally with a mixed-integer program (MIP) and a novel method called Temporal Optimization Planning (TOP). We evaluate the performance of each of these techniques on a dataset of real-world instances from our industrial collaborator, and show that Automated Planning scales to the size of problems faced by industry.

  • ICAPS - Automated Planning for liner shipping fleet repositioning
    2012
    Co-Authors: Kevin Tierney, Amanda Coles, Andrew Coles, Christian Kroer, Adam M Britt, Rune Moller Jensen
    Abstract:

    The Liner Shipping Fleet Repositioning Problem (LSFRP) poses a large financial burden on liner shipping firms. During repositioning, vessels are moved between services in a liner shipping network. The LSFRP is characterized by chains of interacting activities, many of which have costs that are a function of their duration; for example, sailing slowly between two ports is cheaper than sailing quickly. Despite its great industrial importance, the LSFRP has received little attention in the literature. We show how the LSFRP can be solved sub-optimally using the planner POPF and optimally with a mixed-integer program (MIP) and a novel method called Temporal Optimization Planning (TOP). We evaluate the performance of each of these techniques on a dataset of real-world instances from our industrial collaborator, and show that Automated Planning scales to the size of problems faced by industry.

Thomas Leo Mccluskey - One of the best experts on this subject based on the ideXlab platform.

  • ICAPS - Embedding Automated Planning within Urban Traffic Management Operations
    2020
    Co-Authors: Thomas Leo Mccluskey, Mauro Vallati
    Abstract:

    This paper is an experience report on the results of an industry-led collaborative project aimed at automating the control of traffic flow within a large city centre. A major focus of the automation was to deal with abnormal or unexpected events such as roadworks, road closures or excessive demand, resulting in periods of saturation of the network within some region of the city. We describe the resulting system which works by sourcing and semantically enriching urban traffic data, and uses the derived knowledge as input to an Automated Planning component to generate light signal control strategies in real time. This paper reports on the development surrounding the Planning component, and in particular the engineering, configuration and validation issues that arose in the application. It discusses a range of lessons learned from the experience of deploying Automated Planning in the road transport area, under the direction of transport operators and technology developers.

  • K-CAP - Engineering Knowledge for Automated Planning: Towards a Notion of Quality
    Proceedings of the Knowledge Capture Conference on - K-CAP 2017, 2017
    Co-Authors: Thomas Leo Mccluskey, Tiago Stegun Vaquero, Mauro Vallati
    Abstract:

    Automated Planning is a prominent Artificial Intelligence challenge, as well as being a common capability requirement for intelligent autonomous agents. A critical aspect of what is called domain-independent Planning, is the application knowledge that must be added to the planner to create a complete Planning application. This is made explicit in (i) a domain model, which is a formal representation of the persistent domain knowledge, and (ii) an associated problem instance, containing the details of the particular problem to be solved. Both these components are used by Automated Planning engines for reasoning, in order to synthesize a solution plan. Formulating knowledge for use in Planning engines is currently something of an ad-hoc process, where the skills of knowledge engineers significantly influence the quality of the resulting Planning application. On top of that, a notion of quality of the knowledge captured within a domain model is missing; it is therefore hard to provide useful guidelines to knowledge engineers. This paper raises some issues relating to the engineering of application knowledge for Automated Planning, focussing on the domain model. It uses the idea of a domain model as a formal specification of a domain, and considers what it means to measure the quality of such a specification. To do this it proposes definitions of the attributes of a domain model and its encoding language, which are needed by the Automated Planning community in order to improve tools for supporting the engineering of Planning knowledge, and to advance toward a shared and inclusive definition of quality of domain models.

  • IJCAI - Automated Planning for Urban Traffic Management
    Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017
    Co-Authors: Thomas Leo Mccluskey, Mauro Vallati, Santiago Franco
    Abstract:

    The global growth in urbanisation increases the demand for services including road transport infrastructure, presenting challenges in terms of mobility. Optimising the exploitation of urban road network, while attempting to minimise the effects of traffic emissions, is a great challenge. SimplyfAI was a UK research council grant funded project which was aimed towards solving air quality problems caused by road traffic emissions. Large cities such as Manchester struggle to meet air quality limits as the range of available traffic management devices is limited. In the study, we investigated the application of linked data to enrich environmental and traffic data feeds, and we used this with Automated Planning tools to enable traffic to be managed at a region level. The management will have the aim of avoiding air pollution problems before they occur. This demo focuses on the Planning component, and in particular the engineering and validation aspects, that were pivotal for the success of the project

  • Automated Planning for Urban Traffic Control: Strategic Vehicle Routing to Respect Air Quality Limitations
    Intelligenza Artificiale, 2016
    Co-Authors: Lukáš Chrpa, Thomas Leo Mccluskey, Daniele Magazzeni, Keith Mccabe, Mauro Vallati
    Abstract:

    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.

  • Modelling road traffic incident management problems for Automated Planning
    IFAC Proceedings Volumes, 2012
    Co-Authors: Mohammad Munshi Shahin Shah, Thomas Leo Mccluskey, Peter Gregory, Falilat Jimoh
    Abstract:

    Abstract This paper is concerned with the application of Automated Planning in assisting the decision making and logistics in the area of Road Traffic Incidents. The characteristics of this area are that goals must be posed and plans must be output in real time. The domain is complex, with road topology, information distribution, traffic flows, driver behaviour, and highway management controls all potential factors. The representation and encoding of such domain knowledge, of possible actions and plans, and of potential tasks for the road traffic accident scenario is thus a crucial but difficult issue. The goal of this paper is to explore the potential of Automated Planning with hierarchical object-centred domain models in the application of Road Traffic Incident Management(RTIM).