In this work we start walking the path to a new perspective for viewing cyberwarfare scenarios, by introducing conceptual tools (a formal model) to evaluate the costs of an attack, to describe the theater of operations, targets, missions, actions, plans and assets involved in cyberwarfare attacks. We also describe two applications of this model: autonomous planning leading to automated penetration tests, and attack simulations, allowing a system administrator to evaluate the vulnerabilities of his network.

We propose automatically learning probabilistic Hierarchical Task Networks (pHTNs) in order to capture a user's preferences on plans, by observing only the user's behavior. HTNs are a common choice of representation for a variety of purposes in planning, including work on learning in planning. Our contributions are (a) learning structure and (b) representing preferences. In contrast, prior work employing HTNs considers learning method preconditions (instead of structure) and representing domain physics or search control knowledge (rather than preferences). Initially we will assume that the observed distribution of plans is an accurate representation of user preference, and then generalize to the situation where feasibility constraints frequently prevent the execution of preferred plans. In order to learn a distribution on plans we adapt an Expectation-Maximization (EM) technique from the discipline of (probabilistic) grammar induction, taking the perspective of task reductions as productions in a context-free grammar over primitive actions. To account for the difference between the distributions of possible and preferred plans we subsequently modify this core EM technique, in short, by rescaling its input.

We report on the staging of the third competition on knowledge engineering for AI planning and scheduling systems, held during ICAPS-09 at Thessaloniki, Greece in September 2009. We give an overview of how the competition has developed since its first run in 2005, and its relationship with the AI planning field. This run of the competition focused on translators that when input with some formal description in an application-area-specific language, output solver-ready domain models. Despite a fairly narrow focus within knowledge engineering, seven teams took part in what turned out to be a very interesting and successful competition.

We report on the staging of the third competition on knowledge engineering for AI planning and scheduling systems, held during ICAPS-09 at Thessaloniki, Greece in September 2009. We give an overview of how the competition has developed since its first run in 2005, and its relationship with the AI planning field. This run of the competition focused on translators that when input with some formal description in an application-area-specific language, output solver-ready domain models. Despite a fairly narrow focus within knowledge engineering, seven teams took part in what turned out to be a very interesting and successful competition.

These formalisms have in common the use of certain kinds of constructs (for example, objects, goals, skills, and tasks) that represent knowledge of varying degrees of complexity and that are connected through structural relations. In recent years, we have observed increasing interest toward the problem of learning such structural knowledge from observations. These observations range from traces generated by an automated planner to video feeds from a robot performing some actions. The goal of the workshop was to bring researchers together from machine learning, automated planning, case-based reasoning, cognitive science, and other communities that are looking into instances of this problem and to share ideas and perspectives in a common forum.

In discrete dynamical systems change results from actions. As such, given a set of observations, diagnoses often take the form of posited events that result in the observed behaviour. In this paper we revisit formal characterizations of diagnosis, and their relationship to planning. We do so from both a theoretical and a computational perspective. In particular, we extend the characterization of diagnosis to deal with the case of incomplete information, and rich preferences. We also explore the use of state-of-the-art planning technology for the automated generation of diagnoses. Examining several classes of diagnosis problems, we provide both proof of concept and benchmark experiments, the latter showing superior performance to a leading diagnosis engine. Our findings help support the hypothesis that planning technology holds great promise for efficient generation of diagnoses.

A plan with rich control structures like branches and loops can usually serve as a general solution that solves multiple planning instances in a domain. However, the correctness of such generalized plans is non-trivial to define and verify, especially when it comes to whether or not a plan works for all of the infinitely many instances of the problem. In this paper, we give a precise definition of a generalized plan representation called an FSA plan, with its semantics defined in the situation calculus. Based on this, we identify a class of infinite planning problems, which we call one-dimensional (1d), and prove a correctness result that 1d problems can be verified by finite means. We show that this theoretical result leads to a practical algorithm that does this verification practically, and a planner based on this verification algorithm efficiently generates provably correct plans for 1d problems.

The utility of including loops in plans has been long recognized by the planning community. Loops in a plan help increase both its applicability and the compactness of representation. However, progress in finding such plans has been limited largely due to lack of methods for reasoning about the correctness and safety properties of loops of actions. We present novel algorithms for determining the applicability and progress made by a general class of loops of actions. These methods can be used for directing the search for plans with loops towards greater applicability while guaranteeing termination, as well as in post-processing of computed plans to precisely characterize their applicability. Experimental results demonstrate the efficiency of these algorithms.

We present a new paradigm for planning by learning, where the planner is given a model of the world and  a small set of states of interest, but no indication of optimal actions in these states. The additional information can help focus the planner on regions of the state space that are of interest and lead to improved performance. We demonstrate this idea by introducing novel model-checking reduction operations for First Order Decision Diagrams (FODD), a representation that has been used to implement decision-theoretic planning with Relational Markov Decision Processes (RMDP). Intuitively, these reductions modify the construction of the value function by removing any complex specifications that are irrelevant to the set of training examples, thereby focusing on the region of interest. We show that such training examples can be constructed on the fly from a description of the planning problem thus we can bootstrap to get a self-taught planning system. Additionally, we provide a new heuristic to embed universal and conjunctive goals within the framework of RMDP planners, expanding the scope and applicability of such systems. We show that these ideas lead to significant improvements in performance in terms of both speed and coverage of the planner, yielding state of the art planning performance on problems from the International Planning Competition.

Factored planning methods aim to exploit locality to efficiently solve large but "loosely coupled" planning problems by computing solutions locally and propagating limited information between components. However, all factored planning methods presented so far work with representations that require certain parameters to be bounded (e.g. number of coordination points between local plans considered); the satisfaction of those bounds by a given problem instance is difficult to establish a priori, and the influence of those parameters on the problem complexity is unclear. We present an instance of the factored planning framework using a representation of the (regular) sets of local plans by finite automata, which does not require any such bound. By substituting weighted automata, we can even do factored cost-optimal planning. We test an implementation of the method on the few standard planning benchmarks that we have found to be amenable to factoring. We show that this method runs in polynomial time under conditions similar to those considered in previous work, but not only under those conditions. Thus, what constitutes an essential measure of "factorability" remains obscure.