transfer problem-solving experience from previous tasks into the new task. Recently, the artificial intelligence community has attempted to model this transfer in an effort to improve learning on new tasks by using knowledge from related tasks. For example, classification and inference algorithms have been extended to support transfer of conceptual knowledge (for a survey see Torrey and Shavlik...

Planning with SAT has long been viewed as a main approach to AI planning. In comparison to other approaches, its high memory requirements have been considered to be a main obstacle to its scalability to large planning problems. Better implementation technology, especially addressing the memory use, together with a shift of understanding about SAT-based planning during the past ten years, enable...

Existing plan synthesis approaches in artiicial intelligence fall into two categories { domain independent and domain dependent. The domain independent approaches are applicable across a variety of domains, but may not be very eecient in any one given domain. The domain dependent approaches need to be (re)designed for each domain separately, but can be very eecient in the domain for which they ...

Introduction. State-of-the-art robotic systems [1, 2, 3] increasingly rely on search-based planning or optimal control methods to guide decision making. Similar observations can be made about computer game engines. Such methods are nearly always extremely crude approximations to the reality encountered by the robot: they consider a simplified model of the robot (as a point, or a “flying brick”)...

Axioms can be used to model derived predicates in domainindependent planning models. Formulating models which use axioms can sometimes result in problems with much smaller search spaces and shorter plans than the original model. Previous work on axiom-aware planners focused solely on statespace search planners. We propose axiom-aware planners based on answer set programming and integer prorammi...

Introduction The complexity of a task faced by a planner depends strongly on the search space. The importance of the planner’s search space is reflected in the two main classes of classical AI planners: state-space planners, which explore the search space by considering totally ordered sequences of directly neighboring states of the world, and plan-space planners, which explore the search space...

This article advocates the need for strategic spatial planning practices to cope with crises of climate change that confront cities and regions today. Planners face sustainability challenges regarding their ability plan tomorrow. The aim this is analyse how institutional conditions can enable planners’ sustainable development in Swedish municipalities. We will use capacity as a theoretical fram...

Strategic planners for robots designed to operate in a dynamic environment must be able to decide (i) how often a sensory request should be granted, and (ii) how to recover from a detected error. This paper derives closed-form formulas for the appropriate frequency of sensor integration as a function of parameters of the equipment, the domain, and the types of errors from which the system wishe...

Current planners show impressive performance in many real world and artificial domains by using planning (either domain dependent or independent) heuristics. But, on one hand, domain dependent planners still outperform domain independent planners by re-defining domain theories, also including control knowledge. On the other hand, these domain dependent planners require a careful and manual refi...