Using prototypical objects to implement shared behavior in object-oriented systems
OOPLSA '86 Conference proceedings on Object-oriented programming systems, languages and applications
Principles of artificial intelligence
Principles of artificial intelligence
Intelligent integration of information
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Communications of the ACM
On the computational complexity of temporal projection, planning, and plan validation
Artificial Intelligence
Query caching and optimization in distributed mediator systems
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Distributed object computing platforms
Communications of the ACM
Supporting valid-time indeterminacy
ACM Transactions on Database Systems (TODS)
Heterogeneous active agents, I: semantics
Artificial Intelligence
Principles of Database and Knowledge-Base Systems: Volume II: The New Technologies
Principles of Database and Knowledge-Base Systems: Volume II: The New Technologies
Heterogeneous Agent Systems
Logic-based techniques in data integration
Logic-based artificial intelligence
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Logistics deals with the problem of getting the right “stuff” (people, materials, supplies) to the right “place” at the right “time”. Major transportation vendors such as Fedex, UPS, Emery Worldwide, require the efficient solution of logistics problems on a minute by minute basis. Major corporate organizations such as Ford and GM, need to keep inventories at optimal levels, while supporting the smooth inflow of production materials, and smooth outflow of finished products. During the last few years, it has been widely recognized that the next generation of logistics products will, in reality, be powerful information systems that manipulate massive, distributed, logistics databases, and enable logisticians to perform a variety of functions such as tracking the status of supplies and materials, planning based on the current status, efficiently tracking status changes as they occur, and replanning as needed in order to accomplish the mission(s) at hand. In 1996, the Defense Advanced Research Projects Agency (DARPA) started an 80 million dollar research effort called the Advanced Logistics Project (ALP) aimed at developing the next generation of logistics systems. In this paper, we will describe the goals of ALP, describe the multi-agent logistics architecture proposed by ALP, and show how this architecture supports the achievement of ALP's goals.