Paper Info
Title
Towards Autonomous AI Systems for Resource Management: Applications in Industry and Lessons Learned.
Abstract
Complexity of modern resource management is analyzed and related with a number of decision makers, high variety of individual criteria, preferences and constraints, interdependency of all operations, etc. The overview of existing methods and tools of Enterprise Resource Planning is given and key requirements for resource management are specified. The concept of autonomous Artificial Intelligence (AI) systems for adaptive resource management based on multi-agent technology is discussed. Multi-agent model of virtual market and method for solving conflicts and finding consensus for adaptive resource management are presented. Functionality and architecture of autonomous AI systems for adaptive resource management and the approach for measuring adaptive intelligence and autonomy level in these systems are considered. Results of delivery of autonomous AI solutions for managing trucks and factories, mobile teams, supply chains, aerospace and railways are presented. Considerable increase of enterprise resources efficiency is shown. Lessons learned from industry applications are formulated and future developments of AI for solving extremely complex problems of adaptive resource management are outlined.
Year
DOI
Venue
2018
10.1007/978-3-319-94580-4_2
ADVANCES IN PRACTICAL APPLICATIONS OF AGENTS, MULTI-AGENT SYSTEMS, AND COMPLEXITY: THE PAAMS COLLECTION
Keywords
Field
DocType
Complexity,Artificial Intelligence,Autonomous systems,Adaptability,Resource management,Multi-agent technology,Demand-Resource Networks,Self-organization,Competitive equilibriums,Real-time economy
Resource management,Interdependence,Adaptability,Aerospace,Architecture,Enterprise resource planning,Engineering management,Computer science,Supply chain,Autonomous system (Internet)
Conference
Volume
ISSN
Citations 
10978
0302-9743
0
PageRank 
References 
Authors
0.34
0
1
Name
Order
Citations
PageRank
Petr Skobelev111824.61