A Component-oriented Framework for Autonomous Agents

The design of a complex system warrants a compositional methodology, i.e., composing simple components to obtain a larger system that exhibits their collective behavior in a meaningful way. We propose an automaton-based paradigm for compositional design of such systems where an action is accompanied by one or more preferences. At run-time, these preferences provide a natural fallback mechanism for the component, while at design-time they can be used to reason about the behavior of the component in an uncertain physical world. Using structures that tell us how to compose preferences and actions, we can compose formal representations of individual components or agents to obtain a representation of the composed system. We extend Linear Temporal Logic with two unary connectives that reflect the compositional structure of the actions, and show how it can be used to diagnose undesired behavior by tracing the falsification of a specification back to one or more culpable components

The Complexity of Codiagnosability for Discrete Event and Timed Systems

In this paper we study the fault codiagnosis problem for discrete event systems given by finite automata (FA) and timed systems given by timed automata (TA). We provide a uniform characterization of codiagnosability for FA and TA which extends the necessary and sufficient condition that characterizes diagnosability. We also settle the complexity of the codiagnosability problems both for FA and TA and show that codiagnosability is PSPACE-complete in both cases. For FA this improves on the previously known bound (EXPTIME) and for TA it is a new result. Finally we address the codiagnosis problem for TA under bounded resources and show it is 2EXPTIME-complete.Comment: 24 pages

Diagnosis of plan execution and the executing agent

Abstract. We adapt the Model-Based Diagnosis framework to perform (agentbased) 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.

Diagnosis of higher-order discrete-event systems

Preventing major events, like the India blackout in 2012 or the Fukushima nuclear disaster in 2011, is vital for the safety of society. Automated diagnosis may play an important role in this prevention. However, a gap still exists between the complexity of systems such these and the effectiveness of state-of-the-art diagnosis techniques. The contribution of this paper is twofold: the definition of a novel class of discrete-event systems (DESs), called higher-order DESs (HDESs), and the formalization of a relevant diagnosis technique. HDESs are structured hierarchically in several cohabiting subsystems, accommodated at different abstraction levels, each one living its own life, as happens in living beings. The communication between subsystems at different levels relies on complex events, occurring when specific patterns of transitions are matched. Diagnosis of HDESs is scalable, context-sensitive, and in a way intelligent