We address the problem of generalizing a plan to maximize the flexibility and robustness with which an agent can execute it. A key area of automated planning is the study of how to generate a plan for an agent to execute. The plan itself may take on many forms: a sequence of actions, a partial ordering over a set of actions, or a procedure-like description of what the agent should do. Many form...

The Foundation for Intelligent Physical Agents (FIPA) provides a rich set of standards for implementing industrial scale multi-agent infrastructures. Despite its manifold possibilities for achieving coordinated action execution based on interaction protocols, it still lacks direct support of multi-agent planning and scheduling for goal directed action execution. In this paper, we discuss a desi...

This paper is focused on the representation of oncology treatment protocols by a temporally extended, Hierarchical Task Networks (HTN) based knowledge representation as well as their interpretation by a temporal HTN planning process. The planning process allows to obtain temporally annotated therapy plans that support decisions of oncologists in the area of paediatrics oncology.

Most current planning research relies on the assumption of a benign domain containing only cooperative agents. There has been little work on applying modern planning techniques to adversarial domains where this assumption does not hold. This paper discusses the development of an HTN Adversarial Planning architecture and the issues involved in successfully applying such a goal driven approach in...

During the planning process, a planner may often have many different options for what kind of plan refinement to perform next (for example, what task or goal to work on next, what operator or method to use to achieve the task or goal, or how to resolve a conflict or enforce some constraint in the plan). The planner’s efficiency depends greatly on how well it chooses among these options. In this...

Today’s context-aware systems tend to be reactive or ‘pull’ based the user requests or queries for some information and the system responds with the requested information. However, none of the systems anticipate the user’s intent and behavior, or take into account his current events and activities to pro-actively ‘push’ relevant information to the user. On the other hand, Proactive context-awar...

There are several formalizations for hierarchical planning. Many of them allow to specify preconditions and effects for compound tasks. They can be used, e.g., to assist during the modeling process by ensuring that the decomposition methods’ plans “implement” the compound tasks’ intended meaning. This is done based on so-called legality criteria that relate these preconditions and effects to th...

Many real-world planning applications have to deal with resource allocation problems, and so does planning in the domain of crisis management assistance. In order to support resource allocation in these kind of applications, we present a new approach to the integration of scheduling capabilities and planning. The proposed methodology relies on a hybrid planner, which combines action and state a...

We describe HTN-Maker, an algorithm for learning hierarchical planning knowledge in the form of task-reduction methods for Hierarchical Task Networks (HTNs). HTN-Maker takes as input a set of planning states from a classical planning domain and plans that are applicable to those states, as well as a set of semantically-annotated tasks to be accomplished. The algorithm analyzes this semantic inf...

This paper presents an approach towards probabilistic planning with continuous resources. It adopts stochastic concepts for continuous probabilities and integrates them into a STRIPS-based planning framework. The approach enables the construction of plans that are guaranteed to meet certain probability thresholds w.r.t. the consumption of critical resources. Furthermore, the consumption probabi...