Intelligent Planning: A Decomposition and Abstraction Based Approach
Representation and Basic Algorithms. Analytical Techniques. Useful Supporting Algorithms.
Evolution of machine learning
Collective Resource Reasoning. Planning by Decomposition Global Conflict Resolution. Plan Merging. Properties of Task Reduction Hierarchies.
All ERGO components are now being tested in an orbital and a planetary scenario. This paper discusses the ERGO components, its main characteristics, and how they have been applied to an orbital and a planetary scenario. It provides an overview of the evolution of the ERGO system; its main components and the future extensions planned for it. In this work, we investigate the computation of optimal plans for delete-free tasks using relaxed decision diagrams.
Reformulating Oversubscription Planning Tasks.
Most modern heuristics for classical planning are specified in terms of minimizing the summed operator costs. Heuristics for oversubscription planning OSP , on the other hand, maximize the utility on states. In this work we aim to provide the grounds for the adaptation of existing heuristics for classical planning to the OSP setting.
Intelligent Planning - Qiang Yang - Häftad () | Bokus
To this end, we reformulate the OSP task to a classical planning task extended with an additional operator costs function, reflecting the utility information fully. We exemplify how existing heuristics from classical planning can be adapted to such a setting with a merge-and-shrink heuristic and empirically validate the feasibility of our approach. Its main objective is to provide an autonomous framework for future space robots that will be able to perform its activities without the need of constant human supervision and control.
Future space missions, in particular those aimed at Deep Space or planetary exploration, such as Exomars, or Mars demand a greater level of autonomy. The concept of autonomy applies here to a whole set of operations to be performed on-board without human supervision; for instance, a Martian rover has to avoid getting stuck in the sand while traversing, autonomously recharge its batteries periodically, and communicate with Earth occasionally each sol.
Additionally, it will need to be able to detect serendipitous events e. A deep space probe has to take the right measurements to approach an asteroid, and due to the latency of the communication with Ground, these measurements need to be taken autonomously on board. Orbital space missions have already successfully applied autonomy concepts on board, in particular for autonomous event detection and on- board activities planning. In ERGO we provide a framework for autonomy aimed to cover a wide set of a capabilities, ranging from reactive capabilities i.
This paper will discuss the process of the design of robotic systems using the paradigm provided by this framework applied to two different scenarios: a Sample Fetching Rover SFR , and also an On-Orbit Servicing mission, where a damaged spacecraft can have one or several of its modules replaced autonomously by a servicer spacecraft.
We will describe the methodology, the main problems found, the design decisions taken to overcome these problems, as well as an overview of the final design of both systems. Cost partitioning is a well-known technique to make admissible heuristics for classical planning additive. The optimal cost partitioning of explicit-state abstraction heuristics can be computed in polynomial time with a linear program, but the size of the model is often prohibitive. We study this model from a dual perspective and develop several simplification rules to reduce its size. We use these rules to answer open questions about extensions of the state equation heuristic and their relation to cost partitioning.
Florian Pommerening. Thesis, University of Basel, Switzerland, Date of disputation: Admissible heuristics are the main ingredient when solving classical planning tasks optimally with heuristic search.
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Higher admissible heuristic values are more accurate, so combining them in a way that dominates their maximum and remains admissible is an important problem. The thesis makes three contributions in this area. Extensions to cost partitioning a well-known heuristic combination framework allow to produce higher estimates from the same set of heuristics.
The new heuristic family called operator-counting heuristics unifies many existing heuristics and offers a new way to combine them. Another new family of heuristics called potential heuristics allows to cast the problem of finding a good heuristic as an optimization problem. Both operator-counting and potential heuristics are closely related to cost partitioning.
They offer a new look on cost partitioned heuristics and already sparked research beyond their use as classical planning heuristics. Potential heuristics for state-space search are defined as weighted sums over simple state features. Atomic features consider the value of a single state variable in a factored state representation, while binary features consider joint assignments to two state variables.
Previous work showed that the set of all admissible and consistent potential heuristics using atomic features can be characterized by a compact set of linear constraints. We generalize this result to binary features and prove a hardness result for features of higher dimension. Furthermore, we prove a tractability result based on the treewidth of a new graphical structure we call the context-dependency graph. Finally, we study the relationship of potential heuristics to transition cost partitioning. Experimental results show that binary potential heuristics are significantly more informative than the previously considered atomic ones.
Abstraction heuristics are a popular method to guide optimal search algorithms in classical planning. Cost partitionings allow to sum heuristic estimates admissibly by distributing action costs among the heuristics. We introduce state-dependent cost partitionings which take context information of actions into account, and show that an optimal state-dependent cost partitioning dominates its state-independent counterpart.
We demonstrate the potential of our idea with a state-dependent variant of the recently proposed saturated cost partitioning, and show that it has the potential to improve not only over its state-independent counterpart, but even over the optimal state-independent cost partitioning. Our empirical results give evidence that ignoring the context of actions in the computation of a cost partitioning leads to a significant loss of information. Correlation Complexity of Classical Planning Domains.
We analyze how complex a heuristic function must be to directly guide a state-space search algorithm towards the goal. As a case study, we examine functions that evaluate states with a weighted sum of state features.
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- Artificial Intelligence · University of Basel · Florian Pommerening!
- Machine Learning: What it is and why it matters | SAS.
We measure the complexity of a domain by the complexity of the required features. We analyze conditions under which the search algorithm runs in polynomial time and show complexity results for several classical planning domains. Fast Downward Aidos planner abstract. Florian Pommerening and Jendrik Seipp.
Many heuristics for cost-optimal planning are based on linear programming. We cover several interesting heuristics of this type by a common framework that fixes the objective function of the linear program. Within the framework, constraints from different heuristics can be combined in one heuristic estimate which dominates the maximum of the component heuristics. Different heuristics of the framework can be compared on the basis of their constraints. We present theoretical results on the relation between existing heuristics and experimental results that demonstrate the potential of the proposed framework.
We propose the application of variable neighborhood search to address the problem of local optima during the hill-climbing procedure of iPDB. Florian Pommerening and Malte Helmert. We describe transition normal form TNF for classical planning tasks, where there is a unique goal state and variables occur in an operator precondition iff they appear in the effect.
Tasks can be efficiently converted to TNF, all common planning heuristics are invariant under the transformation, and tasks in normal form are easier to study theoretically. New Optimization Functions for Potential Heuristics. Potential heuristics , recently introduced by Pommerening et al. Every feasible solution for these constraints defines an admissible heuristic, and we can obtain heuristics that optimize certain criteria such as informativeness by specifying suitable objective functions. The original paper only considered one such objective function: maximizing the heuristic value of the initial state.
In this paper, we explore objectives that attempt to maximize heuristic estimates for all states reachable and unreachable , maximize heuristic estimates for a sample of reachable states, maximize the number of detected dead ends, or minimize search effort. We also search for multiple heuristics with complementary strengths that can be combined to obtain even better heuristics. Operator cost partitioning is a well-known technique to make admissible heuristics additive by distributing the operator costs among individual heuristics. Planning tasks are usually defined with non-negative operator costs and therefore it appears natural to demand the same for the distributed costs.
We argue that this requirement is not necessary and demonstrate the benefit of using general cost partitioning. We show that LP heuristics for operator-counting constraints are cost-partitioned heuristics and that the state equation heuristic computes a cost partitioning over atomic projections. We also introduce a new family of potential heuristics and show their relationship to general cost partitioning.
Linear Programming for Heuristics in Optimal Planning. Many recent planning heuristics are based on LP optimization. However, planning experts mostly use LP solvers as a black box and it is often not obvious to them which LP techniques would be most suitable for their specific applications.
To foster the communication between the planning and the optimization community, this paper gives an easily accessible overview over these recent LP-based heuristics, namely the optimal cost partitioning heuristic for abstractions, the post-hoc optimization heuristic, a landmark heuristic, the state-equation heuristic, and a delete relaxation heuristic. All these heuristics fit the framework of so-called operator-counting constraints, which we also present.
Keyder, Hoffmann and Haslum recently showed that the obvious extensions to such effects ruin the nice theoretical properties of LM-Cut. We propose a new method based on context splitting that preserves these properties.