Due to recent booming of UAVs technologies, these are being used in many fields involving complex tasks. Some of them involve a high risk to the vehicle driver, such as fire monitoring and rescue tasks, which make UAVs excellent for avoiding human risks. Mission Planning for UAVs is the process of planning the locations and actions (loading/dropping a load, taking videos/pictures, acquiring information) for the vehicles, typically over a time period. These vehicles are controlled from Ground Control Stations (GCSs) where human operators use rudimentary systems. This paper presents a new Multi-Objective Genetic Algorithm for solving complex Mission Planning Problems (MPP) involving a team of UAVs and a set of GCSs. A hybrid fitness function has been designed using a Constraint Satisfaction Problem (CSP) to check if solutions are valid and Pareto-based measures to look for optimal solutions. The algorithm has been tested on several datasets optimizing different variables of the mission, such as the makespan, the fuel consumption, distance, etc. Experimental results show that the new algorithm is able to obtain good solutions, however as the problem becomes more complex, the optimal solutions also become harder to find.

One of the main obstacles in applying AI planning techniques to real problems is the difficulty to model the domains. Usually, this requires that people that have developed the planning system carry out the modeling phase since the representation depends very much on a deep knowledge of the internal working of the planning tools. On some domains such as Business Process Reengineering (BPR), there has already been work on the definition of languages that allow non-experts entering knowledge on processes into the tools. We propose here the use of one of such BPR languages to enter knowledge on the organisation processes to be used by planning tools. Then, planning tools can be used to semi-automatically generate business process models.As instances of this domain, we will use the workflow modeling tool SHAMASH, where we have exploded its object oriented structure to introduce the knowledge through its user-friendly interface and, using a translator transform it into predicate logic terms. After this conversion, real models can be automatically generated using a planner that integrates Planning and Scheduling, IPSS. We present results in a real workflow domain, the TELEPHONE INSTALLATION (TI) domain.

No abstract

Over the past 20 years, there has been much work in the area of model-based diagnosis (MBD). By this we mean diagnosis systems arising from Computer Science or Artificial Intelligence approaches where a generic software engine is developed to address a large class of diagnosis problems [1], [2]. Later, models are created to apply the engine to a specific problem. These techniques are very attractive, suggesting a vision of machines that repair themselves, reduced costs for all kinds of endeavors, spacecraft that continue their missions even when failing, and so on. This promise inspired a broad range of activity, including our involvement over several years in flying the Livingstone and Livingstone 2 on-board model-based diagnosis and recovery systems as experiments on two spacecraft [3], [4], [5], [6], [7].While a great deal was learned through a variety of applications to simulators, testbeds and flight experiments, no project adopted the technology in operations and the expected benefits have not yet come to fruition. This led us to ask what are the costs of using MBD for the operational scenarios we encountered, what are the benefits, and how do we approach the question of whether the benefits outweigh the costs? How are missions today approaching fault diagnosis and recovery during operations? If we characterize the cost and benefits of using MBD, how would it compare with traditional ways of making a system more robust? How did expectations for MBD compare to benefits seen in the field and why?The literature does provide existing cost models for related endeavors such as integrated vehicle health management [8], [9], [10]. It also provides excellent narratives of why projects chose not to use MBD after considering it [11]. However, we believe that this paper is the first to unpack and discuss the cost, benefit and risk factors that impact the net value of model-based diagnosis and recovery. We use experience with systems such as Livingstone as an example, so our focus is on-board model-based diagnosis and recovery, but we believe many of the insights and remaining questions on the costs and benefits are applicable to other diagnosis applications.While the analysis is not yet mature enough to provide a 1-4244-1488-quantitative model of when on-board model-based diagnosis would be an effective choice, it lays out the cost/benefit proposition and identifies several disconnects that we believe prevent adoption as an operational tool. While we do not suggest metrics for every cost, benefit and risk factor we identify, we do discuss where each factor arises in development or operations and how model-based diagnosis and recovery tends to leverage or exacerbate each. As such we believe the analysis is of use to those developing MBD or related techniques and those who may employ them. It also serves as one example of how honest expectations based on technical capability can come to differ from the net impact on customer problems.In this paper we present a cost/benefit analysis for MBD, using expectations and experie...

Data Extraction from the World Wide Web is a well known, non solved, and a critical problem when complex information systems are designed. These problems are related to the extraction, management and reuse of the huge amount of Web data available. These data have usually a high heterogeneity, volatility and low quality (i.e. format and content mistakes), so it is quite hard to build realible systems. In this chapter we propose an updated state of the art revision of the problem of Web Data Extraction, and an Evolutionary Computation approach based on Genetic Algorithms and Regular Expressions to the problem of automatically learn software entities. These entities, also called wrappers, will be able to extract some kind of Web data structures from examples.