https://www.cs.nmsu.edu/~tson/classes/spring04-579/laborie-resources-aij.pdf

Algorithms for propagating resource constraints in AI planning and scheduling: existing approaches and new results

This paper summarizes the main existing approaches to propagate resource constraints in Constraint-Based scheduling and identifies some of their limitations for using them in an integrated planning and scheduling framework. We then describe two new algorithms to propagate resource constraints on discrete resources and reservoirs. Unlike most of the classical work in scheduling, our algorithms focus on the precedence relations between activities rather than on their absolute position in time. They are efficient even when the set of activities is not completely defined and when the time window of activities is large. These features explain why our algorithms are particularly suited for integrated planning and scheduling approaches. All our algorithms are illustrated with examples. Encouraging preliminary results are reported on pure scheduling problems.

Algorithms for Propagating Resource Constraints in AI Planning and Scheduling: Existing Approaches and New Results

Inverse reinforcement learning (IRL) is the problem of recovering the underlying reward function from the behaviour of an expert. Most of the existing algorithms for IRL assume that the expert's environment is modeled as a Markov decision process (MDP), although they should be able to handle partially observable settings in order to widen the applicability to more realistic scenarios. In this paper, we present an extension of the classical IRL algorithm by Ng and Russell to partially observable environments. We discuss technical issues and challenges, and present the experimental results on some of the benchmark partially observable domains.

/pdf/inverse-reinforcement-learning-in-partially-observable-4civlivt45.pdf

Inverse reinforcement learning in partially observable environments

There are many systems and techniques that address stochastic planning and scheduling problems, based on distinct and sometimes opposite approaches, especially in terms of how generation and execution of the plan, or the schedule, are combined, and if and when knowledge about the uncertainties is taken into account. In many real-life problems, it appears that many of these approaches are needed and should be combined, which to our knowledge has never been done. In this paper, we propose a typology that distinguishes between proactive, progressive, and revision approaches. Then, focusing on scheduling and schedule execution, a theoretic model integrating those three approaches is defined. This model serves as a general template to implement a system that will fit specific application needs: we introduce and discuss our experimental prototypes which validate our model in part, and suggest how this framework could be extended to more general planning systems.

/pdf/a-theoretic-and-practical-framework-for-scheduling-in-a-1qecygy89y.pdf

A theoretic and practical framework for scheduling in a stochastic environment

http://eil.utoronto.ca/wp-content/uploads/scheduling/papers/beckfox-aijv121.pdf

Constraint-directed techniques for scheduling alternative activities

The central thesis of this dissertation is that an understanding of the structure of a problem leads to high-quality heuristic problem solving performance in constraint-directed scheduling. Our methods for gaining an understanding of problem structure focus on texture measurements: algorithms that implement dynamic analyses of each search state. Texture measurements distill structural information from the constraint graph representation of a search state which is then used as a basis for heuristic decision-making. 
To enable the rigorous empirical investigation of the above thesis in the context of real-world scheduling problems, we define the ODO framework for constraint-directed search. The framework allows us to implement scheduling algorithms and their components, and to compare them both from the perspective of static similarities and on the basis of empirical performance. 
In this dissertation, we extend the scope of scheduling problems that can be addressed, in general, by constraint-directed techniques. Specifically, we develop the concept of a generalized measure of constraint criticality that enables the construction of dynamic, opportunistic heuristic commitment techniques. Based on an analysis of the requirements of a measure of constraint criticality, we suggest the probability of breakage of a constraint as such a measure. 
The investigation of our thesis focuses on three classes of scheduling problems: job shop scheduling, scheduling with inventory, and scheduling with alternative activities. In each of these problem classes we empirically demonstrate that, as a problem becomes more complex, knowledge of its structure has a dominant role in guiding heuristic search to a solution.

/pdf/texture-measurements-as-a-basis-for-heuristic-commitment-5d1vcg78r4.pdf

Texture measurements as a basis for heuristic commitment techniques in constraint-directed scheduling

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

John Christopher Beck

Author Tools

Chat about Author

Papers

Texture measurements as a basis for heuristic commitment techniques in constraint-directed scheduling