Strategies for Non-Deterministic Planning Problems

This article discusses how to create general strategies for solving planning problems in environments where actions can lead to different outcomes. It focuses on a specific kind of planning called Fully Observable Non-Deterministic (FOND) planning.

#What Are General Policies?

General policies are broad strategies that can be applied to many similar problems. They are not tied to one specific situation but can adapt to different instances of a problem within a set framework. By learning these policies from a small number of training examples, we can efficiently tackle larger groups of problems.

#Extending Learning Methods

The methods used to create these general policies have been effective in simpler planning environments. This work aims to expand those methods to handle more complex situations found in FOND domains. In FOND planning, action outcomes are uncertain, meaning the same action can lead to different results depending on the situation.

#Evaluating the Approach

To assess the effectiveness of the proposed methods, tests were run across various benchmark problems. The results showed that the learned strategies can solve many FOND challenges and that their correctness can be verified.

#The Relation of FOND to Other Planning Types

FOND planning relates closely to Classical Planning and Markov Decision Problems (MDPs). Understanding this connection helps in formulating effective strategies. For example, the best FOND planners often utilize classical planners to improve performance. However, while classical planning problems have clear solutions, FOND problems may require more sophisticated approaches due to their complexity.

#How FOND Planning Works

In a FOND planning problem, actions can lead to multiple possible future states. This uncertainty complicates finding a solution, but it is possible to describe these problems using a structured framework. Different conditions and consequences are considered to establish a clear path to success.

#Dead-End States

A crucial aspect of FOND problems is the idea of "dead-ends," or situations where no further actions can lead to a solution. Identifying these states is essential as they hinder progress. Finding strategies that avoid these dead-end states can lead to successful outcomes.

#Classical Planning Basics

Classical planning involves creating a sequence of actions that takes you from an initial state to a goal state. The process includes defining the actions available, the conditions that must be met, and the effects of those actions. This framework is essential for more complex types of planning, including FOND.

#Generalized Classical Planning

Generalized classical planning differs from traditional classical planning by allowing for the creation of policies that can be applied across a class of related problems. This approach enables the development of a single strategy that can handle multiple variations of a problem.

#FOND Planning Models

A FOND model consists of the states, actions, and transitions between those states. Unlike classical planning, where outcomes are deterministic, FOND models incorporate the possibility of multiple outcomes for a given action. This aspect of FOND planning makes it more dynamic and complex.

#Learning Policies from Training Instances

The core of this research is the ability to learn general policies from small collections of training instances. By analyzing successful actions and their consequences, effective strategies can be developed for a broader range of situations.

#Related Work on General Policies

The field of learning general policies has a rich history. Many approaches have developed over the years, including logical formulations and feature-based learning. Some methods have also used deep learning techniques, but their results are often hard to interpret compared to more transparent combinatorial methods.

#The Importance of Transparency in Policies

Transparency in the strategies being developed is crucial. It allows for easier assessment of their correctness and applicability. The methods proposed in this work aim to balance the need for effective policy learning with the need for clarity in how those policies operate.

#FOND Planning Techniques

Several techniques have been used in FOND planning, including graph search strategies and SAT solving. These methods can efficiently navigate the complexities of FOND problems, leading to effective solutions.

#Dead-End Representation Learning

Representing dead-end states is a key part of developing effective strategies. By correctly identifying when an action leads to a dead end, planners can avoid those paths and focus on more promising routes.

#Constructing Classical Planning Problems

When modeling classical planning problems, specific parameters are defined, including the initial state and the desired goal. Each parameter plays a role in determining the sequence of actions that will lead to a solution.

#Non-Deterministic Policies

In FOND planning, non-deterministic policies offer a way to handle the uncertainty inherent in the problem. These policies map states to actions, providing a clear guide for making decisions even when outcomes are not guaranteed.

#Features in Policy Learning

Features play a significant role in policy learning as they help define the conditions that influence decisions. By selecting relevant features, planners can improve their chances of developing effective strategies.

#Propositional Theory in Planning

The propositional theory serves as the foundation for many planning methods. It allows planners to express their policies and constraints in a structured manner, facilitating the learning process.

#The Role of State Constraints

Incorporating constraints into the planning process ensures that certain conditions are met during execution. This aspect is essential for avoiding dead ends and ensuring successful outcomes.

#Incremental Learning of Policies

An incremental approach to learning allows for the gradual improvement of strategies based on performance. By testing policies against increasing complexity, planners can identify which strategies are most effective.

#The Process of Selecting Features

Selecting features effectively is key to learning successful policies. The process involves evaluating various features to see which ones contribute positively to the development of a general policy.

#Constructing the Feature Pool

The feature pool is a collection of potential features that can be used in the learning process. By building this pool systematically, planners can ensure they have a rich set of options to choose from.

#Experimental Setup

The experiments conducted used various benchmarks to test the proposed methods rigorously. These tests provided valuable insights into the effectiveness of the learning strategies.

#Results of the Experiments

The results from the experiments highlighted the strengths and weaknesses of the proposed methods. Many strategies successfully solved a range of problems, demonstrating their practical applicability.

#Analyzing the Learned Policies

After the experiments, it is crucial to analyze the learned policies to understand their behavior in different situations. This analysis provides insights into their effectiveness and areas for improvement.

#Conclusions

The work presented offers a promising approach to learning general policies for planning problems in non-deterministic environments. By focusing on key aspects such as dead-end avoidance, feature selection, and the use of combinatorial methods, effective strategies can be developed that apply across a wide range of situations. Future research can build on these findings to further improve policy learning in complex planning domains.