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Front tracking methods can be used to accurately resolve discontinuities in numerical simulations of Euler flows. They usually result in first-order error convergence due to their piecewise constant approximation of the flow conditions. In this chapter, a piecewise linear reconstruction of the solution is proposed based on wave types which track the physical phenomena that the fronts represent. It is demonstrated numerically that this approach results in second-order error convergence. A verification and validation study is performed by comparing the results with those of the Godunov method and experimental data. [ABSTRACT FROM AUTHOR]

We discuss task planning problems where a number of agents have to work on a joint planning problem that consists of a set of interdependent, hierarchically ordered tasks. Each agent is assigned a subset of tasks to perform for which it has to construct a plan. The agents are non-cooperative in that they insist on planning autonomously and do not want to revise their individual plans when a joint plan has to be assembled. The aim of this paper is twofold: first of all to present a general formal framework to study some computational aspects of this non-cooperative coordination problem, and secondly to establish some complexity results and to identify some of the factors that contribute to the complexity of this problem. [ABSTRACT FROM AUTHOR]

We discuss the application of Model-Based Diagnosis in (agent- based) planning. Here, a plan together with its executing agent is considered as a system to be diagnosed. It is assumed that the execution of a plan can be monitored by making partial observations of the results of actions. These observations are used to explain the observed deviations from the plan by qualifying some action instances that occur in the plan as behaving abnormally. Unlike in standard model-based diagnosis, however, in plan diagnosis we cannot assume that actions fail independently. We focus on two sources of dependencies between failures: such failings may occur as the result of malfunctioning of the executing agent or may be caused by dependencies between action instances occurring in a plan. Therefore, we introduce causal rules that relate health states of the agent and health states of actions to abnormalities of other action instances. These rules enable us to determine the underlying causes of plan failing and to predict future anomalies in the execution of actions. [ABSTRACT FROM AUTHOR]

We adapt the Model-Based Diagnosis framework to perform (agent-based) plan diagnosis. In plan diagnosis, the system to be diagnosed is a plan, consisting of a partially ordered set of instances of actions, together with its executing agent. The execution of a plan can be monitored by making partial observations of the results of actions. Like in standard model-based diagnosis, observed deviations from the expected outcomes are explained qualifying some action instances that occur in the plan as behaving abnormally. Unlike in standard model-based diagnosis, however, in plan diagnosis we cannot assume that actions fail independently. We focus on two sources of dependencies between failures: dependencies that arise as a result of a malfunction of the executing agent, and dependencies that arise because of dependencies between action instances occurring in a plan. Therefore, we introduce causal rules that relate health states of the agent and health states of actions to abnormalities of other action instances. These rules enable us to introduce causal set and causal effect diagnoses that use the underlying causes of plan failing to explain deviations and to predict future anomalies in the execution of actions. Keywords: planning, diagnosis, agent systems. [ABSTRACT FROM AUTHOR]