Attack graph is a well-known representation for computer security vulnerabilities, which captures how malicious activities can lead to a system compromise. A key weakness in the attack graph representation is that it scales poorly, particularly in large domains where the graph needs to enumerate both user and system interactions. One way to address this problem is to translate the attack graph ...

Graphplan planning graphs are structures widely used in modern planners. The exclusion relations calculated in the planning graph extension provide very useful information, especially in temporal planning where actions have different duration. However, Graphplan backward search has some inefficiencies that impose limitations when dealing with large temporal problems. This paper presents a new s...

This paper presents a graph-based backtracking algorithm designed to support constraintbased planning in data production domains. This algorithm performs backtracking at two nested levels: the outerbacktracking following the structure of the planning graph to select planner subgoals and actions to achieve them and the inner-backtracking inside a subproblem associated with a selected action to f...

The capability of using cached plans, whether in the form of expert provided demonstrations or previously generated paths from prior planner runs, is a useful addition to any search based planner. Its benefits range from faster planning times in large state spaces to generation of more predictable replans in case of partially known environments which robots discover as they plan. The experience...

We describe a planning system in development for efficiently and scalably distributing the task of complex object assembly among multi-robot or human-robot teams. The planner utilizes an AND/OR graph to concisely represent feasible distributed assembly sequences for a given number of available workers (robots or humans). By representing all possible assembly plans in the AND/OR graph, the plann...

Graphplan planning graphs are structures widely used in modern planners. The exclusion relations calculated in the planning graph extension provide very useful information, especially in temporal planning where actions have different duration. However, Graphplan backward search has some inefficiencies that impose limitations when dealing with large temporal problems. This paper presents a new s...

We examine the issues that arise in extending an estimatedregression planner to find plans for multiagent teams, cooperating agents that take orders but do no planning themselves. An estimated-regression system is a classical planner that searches situation space, using as a heuristic numbers derived from a backward search through a simplified space, summarized in the regression-match graph. Ex...

This short paper provides a high-level description of the planner CBP (Cost-Based Planner). CBP performs heuristic search in the state space using several heuristics. On one hand it uses look-ahead states based on relaxed plans to speed-up the search; on the other hand the search is also guided using a numerical heuristic and a selection of actions extracted from a relaxed planning graph. The r...

This paper describes the planner BRT (Biased Rapidlyexploring Tree). This planner is basically a Rapidlyexploring Random Tree (RRT) adapted to automated planning that employs Fast-Downward as the base planner. The novelty in this case is that it does not sample the search space in a random way; rather, it estimates which propositions are more likely to be achieved along some solution plan and u...

This paper presents a study on the transfer of learned control knowledge between two different planning techniques. We automatically learn heuristics (usually, in planning, heuristics are also named control knowledge) from one planner search process and apply them to a different planner. The goal is to improve this second planner efficiency solving new problems, i.e. to reduce computer resource...