International Journal of Cooperative Information Systems

Future information systems will involve large numbers of heterogeneous, intelligent agents distributed over large computer/communication networks. Agents may be humans, humans interacting with computers, humans working with computer support, and computer systems performing tasks without human intervention. We call such systems Intelligent and Cooperative Information Systems (ICISs). Although we can imagine extensions of capabilities of current ISs and of individual contributing core technologies, such as databases, artificial intelligence, operating systems, and programming languages, we cannot imagine the capabilities of ICISs which we believe will be based on extensions of these and other technologies. Neither do we know exactly what technologies and capabilities will be required, what challenges will arise, nor how the technologies might be integrated or work together to address the challenges.

In this paper, we provide initial definitions for key concepts and terms in this new area, identify potential core contributing technologies, illustrate the ICIS concept with example systems, and pose basic research questions. We also describe the results of discussions on these topics that took place at the Second International Workshop on Intelligent and Cooperative Information Systems held in Como, Italy, October 1991. The workshop focused on core technologies for ICISs. The workshop and the results reflect the multi-disciplinary nature of this omerging area.

Research in Cooperative Distributed Problem Solving (CDPS) considers how problem-solving tasks should be allocated among a group of agents and how the agents should coordinate their actions to achieve effective problem solving. For some CDPS systems, negotiation plays an important role in how agents cooperate. We define negotiation as the process of information exchange by which the agents act to resolve inconsistent views and to reach agreement on how they should work together in order to cooperate effectively. We describe a generic model, the Recursive Negotiation Model (RNM), that can serve as a basis for classifying and specifying where conflict resolution among multiple experts, viewpoints, or types of reasoning is needed in building a sophisticated CDPS system. This model defines where and how negotiation can be applied during problem solving based on structuring problem solving into four stages: problem formulation, focus-of-attention, allocation of goals or tasks to agents, and achievement of goals or tasks. We further discuss how the degree of agent participation in control decisions, including decisions about assigning responsibility to agents, influences the nature of negotiation within a particular system. Through this model, we emphasize that negotiation may be a recursive, complex, and pervasive process that is used to resolve conflicts in both domain-level and control-level problem solving. Finally, we survey existing negotiation frameworks and how they relate to our generic model.

The distributed coordination problem can be described as "how should the local scheduling of activities at each agent be affected by non-local concerns and constraints?" Partial global planning (PGP) is a flexible approach to distributed coordination that allows agents to respond dynamically to their current situation. It is based on detecting relationships in the computational goal structures of the distributed agents. However, the detailed PGP mechanisms depend on the existence and availability of certain characteristics and structures that are idiosyncratic to the Distributed Vehicle Monitoring Testbed (DVMT). Generalized Partial Global Planning tries to extend the PGP approach by communicating more abstract and hierarchically organized information, detecting in a general way the coordination relationships that are needed by the partial global planning mechanisms, and separating the process of coordination from local scheduling. This new characterization of partial global planning has less communication overhead and can be more easily adapted and extended to new styles of problem solving and new multi-agent environments that have different characteristics from the original DVMT. This paper first describes the coordination problem as it was viewed by the PGP algorithm, and then extensions to that problem. It then briefly describes our model of task structures and coordination relationships. Finally, we show how the PGP algorithm, as an example, can be described using our method.

Intelligent and Cooperative Information Systems (ICIS) will have large numbers of distributed, heterogeneous agents interacting and cooperating to solve problems regardless of location, original mission, or platform. The agents in an ICIS will adapt to new and possibly surprising situations, preferably without human intervention. These systems will not only control a domain, but also will improve their own performance over time, that is, they will learn.

This paper describes five heterogeneous learning agents and how they are integrated into an Integrated Learning System (ILS) where some of the agents cooperate to improve performance. The issues involve coordinating distributed, cooperating, heterogeneous problem-solvers, combining various learning paradigms, and integrating different reasoning techniques. ILS also includes a central controller, called The Learning Coordinator (TLC), that manages the control of flow and communication among the agents, using a high-level communication protocol. In order to demonstrate the generality of the ILS architecture, we implemented an application which, through its own experience, learns how to control the traffic in a telephone network, and show the results for one set of experiments. Options for enhancements of the ILS architecture are also discussed.

A distributed computer network management system consisting of cooperating autonomous computing agents allows network management to be more responsive due to information gathering and network recovery activities being performed in parallel. However, to perform these tasks, the network of agents requires a stable organizational infrastructure. In addition, to meet the needs of human network administrators, the distributed system must allow ultimate authority to be centralized at a single location. Distributed Big Brother (DBB) represents a pragmatic blending of diverse technologies from the field of distributed AI, such as contract formation, organizational structuring, election for role assignment, and hierarchical control. The result is an infrastructure for a network management system in which separate agents reconfigure themselves when hardware and software failures occur in order to assure the authority structure demanded by network operators. Our efforts illustrate how integrating existing distributed AI technologies can meet realistic needs, and highlight open problems that require the development of new technologies.