Fox, Mark S.


Building Agents to Serve Customers

AI Magazine

AI agents combining natural language interaction, task planning, and business ontologies can help companies provide better-quality and more costeffective customer service. Our customer-service agents use natural language to interact with customers, enabling customers to state their intentions directly instead of searching for the places on the Web site that may address their concern. Our agents converse with customers, guaranteeing that needed information is acquired from customers and that relevant information is provided to them in order for both parties to make the right decision. The net effect is a more frictionless interaction process that improves the customer experience and makes businesses more competitive on the service front.


Building Agents to Serve Customers

AI Magazine

AI agents combining natural language interaction, task planning, and business ontologies can help companies provide better-quality and more costeffective customer service. Our customer-service agents use natural language to interact with customers, enabling customers to state their intentions directly instead of searching for the places on the Web site that may address their concern. We use planning methods to search systematically for the solution to the customer's problem, ensuring that a resolution satisfactory for both the customer and the company is found, if one exists. Our agents converse with customers, guaranteeing that needed information is acquired from customers and that relevant information is provided to them in order for both parties to make the right decision. The net effect is a more frictionless interaction process that improves the customer experience and makes businesses more competitive on the service front.


A Generic Framework for Constraint-Directed Search and Scheduling

AI Magazine

This article introduces a generic framework for constraint-directed search. The research literature in constraint-directed scheduling is placed within the framework both to provide insight into, and examples of, the framework and to allow a new perspective on the scheduling literature. We show how a number of algorithms from the constraint-directed scheduling research can be conceptualized within the framework. This conceptualization allows us to identify and compare variations of components of our framework and provides new perspective on open research issues. We discuss the prospects for an overall comparison of scheduling strategies and show that firm conclusions vis-a-vis such a comparison are not supported by the literature. Our principal conclusion is the need for an empirical model of both the characteristics of scheduling problems and the solution techniques themselves. Our framework is offered as a tool for the development of such an understanding of constraint-directed scheduling and, more generally, constraint-directed search.


Enterprise Modeling

AI Magazine

To remain competitive, enterprises must become increasingly agile and integrated across their functions. Enterprise models play a critical role in this integration, enabling better designs for enterprises, analysis of their performance, and management of their operations. This article motivates the need for enterprise models and introduces the concepts of generic and deductive enterprise models. It reviews research to date on enterprise modeling and considers in detail the Toronto virtual enterprise effort at the University of Toronto.



Constructing and Maintaining Detailed Production Plans: Investigations into the Development of K-B Factory Scheduling

AI Magazine

Human schedulers are typically overburdened by the complexity of this task, and conventional computer-based scheduling systems consider only a small fraction of the relevent knowledge. This article describes research aimed at providing a framework in which all relevant scheduling knowledge can be given consideration during schedule generation and revision. Factory scheduling is cast as a complex constraint-directed activity, driven by a rich symbolic model of the factory environment in which various influencing factors are formalized as constraints. Two knowledge-based factory scheduling systems that implement aspects of this approach are described.


Editorial

AI Magazine

Editorials marking the first time AI Magazine has been devoted to a single theme (the application of AI to manufacturing problems). Editorials marking the first time AI Magazine has been devoted to a single theme (the application of AI to manufacturing problems).


Introducing Carnegie-Mellon University's Robotics Institute (Research in Progress)

AI Magazine

Carnegie-Mellon University has established a Robotics Institute to bring its expertise in engineering, science, and industrial administration to bear upon the problem of national industrial productivity. The institute has been established to undertake advanced research and development in seeing, thinking robots and intelligent systems, and to facilitate transfer of this technology to industry. The Institute is engaged in broad programs of research in robotics, artificial intelligence, manufacturing technology, micro-electronics technology, and computer science. The Institute offers the promise of dramatic advances that will not only improve the productivity of all types of employees but also lead to improvements in the "quality of life" for all.