Planning is motivated by acting. Most of the existing work on automated planning
underestimates the reasoning and deliberation needed for acting; it is instead biased …

In this paper we examine the general problem of generating preferred explanations for
observed behavior with respect to a model of the behavior of a dynamical system. This …

In this paper, we address the problem of finding diverse high-quality plans motivated by the
hypothesis generation problem. To this end, we present a planner called TK* that first …

The task of explanatory diagnosis conjectures actions to explain observations. This is a
common task in real life and an essential ability of intelligent agents. It becomes more …

In this paper we apply AI planning to address the hypothesis exploration problem and
provide assistance to network administrators in detecting malware based on unreliable …

Plan recognition algorithms output hypotheses about an agent's plans from its observed
actions. Due to imperfect knowledge about the agent's behavior and the environment, it is …

We present a conflict-based approach to diagnosing Discrete Event Systems (DES) which
generalises Reiter's Diagnose algorithm to a much broader class of problems. This …

An AI system that is operating in an adversarial environment should be able to provide
safeguards for its internal information. In other words, an adversary should not be able to …

Diagnosis of discrete event systems amounts to finding good explanations, in the form of
system trajectories consistent with a given set of partially ordered observations. This …

A gap still exists between complex discrete-event systems (DESs) and the effectiveness of
the state-of-the-art diagnosis techniques, where faults are defined at component levels and …