Classical control systems utilize implicit analytical models and select inputs using numerical computation in order to achieve desired plant performance in real time. Artificial intelligence planning systems utilize explicit symbolic or logical models of discrete operations and construct consistent planned sequences using symbolic reasoning to achieve specified task goals. In the development of...

Plan generation is important in a number of agent applications, but such applications generally require elaborate domain models that include not only the definitions of the actions that an agent can perform in a given domain, but also information about the most effective ways to generate plans for the agent in that domain. Such models typically take a large amount of human effort to create. To ...

Low-level motion planning techniques must be combined with high-level task planning formalisms in order to generate realistic plans that can be carried out by humans and robots. Previous attempts to integrate these two planning formalisms mostly used either Classical Planning or HTN Planning. Recently, we developed Hierarchical Goal Networks (HGNs), a new hierarchical planning formalism that co...

One difficulty with existing theoretical work on HTN planning is that it does not address some of the planning constructs that are commonly used in HTN planners for practical applications. Although such constructs can make it difficult to ensure the soundness and completeness of HTN planning, they are important because they can greatly improve the efficiency of planning in practice. In this pap...

Hierarchies are the most common structure used to understand the world better. In galaxies, for instance, multiple-star systems are organised in a hierarchical system. Then, governmental and company organisations are structured using a hierarchy, while the Internet, which is used on a daily basis, has a space of domain names arranged hierarchically. Since Artificial Intelligence (AI) planning p...

The ability to plan for multi-step manipulation tasks in unseen situations is crucial future home robots. But collecting sufficient experience data end-to-end learning often infeasible the real world, as deploying robots many environments can be prohibitively expensive. On other hand, large-scale scene understanding datasets contain diverse and rich semantic geometric information. how leverage ...

This article describes SHPE (Simple Hierarchical Planning Engine), a hierarchical task network planning system designed to generate dynamic behaviours for real-time video games. SHPE is based on a combination of domain compilation and procedural task application/decomposition techniques in order to compute plans in a very short time-frame. The planner has been able to return relevant plans in l...

CDMA network planning, for example in 3G UMTS networks, is an important task whether for upgrading existing networks or planning new networks. It is a time consuming, computationally hard, task and generally requires the consideration of both downlink and uplink requirements. Simulation experiments presented here suggest that if time is a major consideration in the planning process then as a co...