Daniel D. Corkill

Trends in cooperative distributed problem solving

The authors present an overview of cooperative distributed problem solving (CDPS), an emerging research area that combines aspects of AI (artificial intelligence) and distributed processing. CDPS can be used to study how a loosely coupled network of sophisticated problem-solving nodes can solve a complex problem which consists of a set of interdependent subproblems. Subproblems arise because of spatial, temporal, and functional distribution of data, knowledge, and processing capabilities. Application areas include distributed interpretation, distributed planning and control, cooperating expert systems, and computer-supported human cooperation. The authors survey the important approaches and empirical investigations that have been developed. The approaches covered include negotiation, functionally accurate cooperation, organizational structuring, multiagent planning, sophisticated local control, and theoretical frameworks. >

Functionally Accurate, Cooperative Distributed Systems

A new approach for structuring distributed processing systems, called functionally accurate, cooperative (FA/C), is proposed. The approach differs from conventional ones in its emphasis on handling distribution-caused uncertainty and errors as an integral part of the network problem-solving process. In this approach nodes cooperatively problem-solve by exchanging partial tentative results (at various levels of abstraction) within the context of common goals. The approach is especially suited to applications in which the data necessary to achieve a solution cannot be partitioned in such a way that a node can complete a task without seeing the intermediate state of task processing at other nodes. Much of the inspiration for the FA/C approach comes from the mechanisms used in knowledge-based artificial intelligence (Al) systems for resolving uncertainty caused by noisy input data and the use of approximate knowledge. The appropriateness of the FA/C approach is explored in three application domains: distributed interpretation, distributed network traffic-light control, and distributed planning. Additionally, the relationship between the approach and the structure of management organizations is developed. Finally, a number of current research directions necessary to more fully develop the FA/C approach are outlined. These research directions include distributed search, the integration of implicit and explicit forms of control, and distributed planning and organizational self-design.

Agent Technologies for Sensor Networks

Wireless sensor networks are increasingly seen as a solution to the problem of performing continuous wide-area monitoring in many environmental, security, and military scenarios.

Automated organization design for multi-agent systems

The ability to create effective multi-agent organizations is key to the development of larger, more diverse multi-agent systems. In this article we present KB-ORG: a fully automated, knowledge-based organization designer for multi-agent systems. Organization design is the process that accepts organizational goals, environmental expectations, performance requirements, role characterizations, and agent descriptions and assigns roles to each agent. These long-term roles serve as organizational-control guidelines that are used by each agent in making moment-to-moment operational control decisions. An important aspect of KB-ORG is its efficient, knowledge-informed search process for designing multi-agent organizations. KB-ORG uses both application-level and coordination-level organization design knowledge to explore the combinatorial search space of candidate organizations selectively. KB-ORG also delays making coordination-level organizational decisions until it has explored and elaborated candidate application-level agent roles. This approach significantly reduces the exploration effort required to produce effective designs as compared to modeling and evaluation-based approaches that do not incorporate design expertise. KB-ORG designs are not restricted to a single organization form such as a hierarchy, and the organization designs described here contain both hierarchical and peer-to-peer elements. We use examples from the distributed sensor network (DSN) domain to show how KB-ORG uses situational parameters as well as application-level and coordination-level knowledge to generate organization designs. We also show that KB-ORG designs effective, yet substantially different, organizations when given different organizational requirements and environmental expectations.

Issues and challenges of knowledge representation and reasoning methods in situation assessment (Level 2 Fusion)

Situation assessment (SA) involves deriving relations among entities, e.g., the aggregation of object states (i.e. classification and location). While SA has been recognized in the information fusion and human factors literature, there still exist open questions regarding knowledge representation and reasoning methods to afford SA. For instance, while lots of data is collected over a region of interest, how does this information get presented to an attention constrained user? The information overload can deteriorate cognitive reasoning so a pragmatic solution to knowledge representation is needed for effective and efficient situation understanding. In this paper, we present issues associated with Level 2 (Situation Assessment) including: (1) user perception and perceptual reasoning representation, (2) knowledge discovery process models, (3) procedural versus logical reasoning about relationships, (4) user-fusion interaction through performance metrics, and (5) syntactic and semantic representations. While a definitive conclusion is not the aim of the paper, many critical issues are proposed in order to characterize future successful strategies to knowledge representation and reasoning strategies for situation assessment.

Designing Integrated Engineering Environments: Blackboard-Based Integration of Design and Analysis Tools

Blackboard-based integration of multiple agents is a natural and viable technology for the implementation of concurrent-engineering (CE) environments This article presents a blackboard architecture that has been extended to support the integration of heterogeneous collabora tive agents The suitability of the architecture as an integration framework for CE applications is discussed, followed by a description of a proto type CE application developed as part of Ford Motor Companys participation in the RRM (Rapid Response Manufacturing) Consortium 1 The prototype is a mixed-initiative, multiuser, multiprocessing environment that integrates heterogeneous agents working on crankshaft design, analy sis, and manufacturing-feasibility assessment The effectiveness of the RRM prototype and the ease and speed with which it was created demon strates that blackboard-based integration technology is appropriate for building multiagent concurrent-engineering applications

Separating domain and coordination in multi-agent organizational design and instantiation

Organizational design and instantiation is the process that accepts a set of organizational goals, performance requirements, agents, and resources and assigns responsibilities and roles to each agent. We present a prescriptive organizational design and instantiation process for multi-agent systems. An important aspect of our approach is the separation of application-specific organizational issues from more generic organizational coordination mechanisms. We describe our model of organizational design and our search process. We also present example organizations generated by our automated system for the distributed sensor network domain under different environmental characteristics and performance requirements.

Embedable problem-solving architectures: a study of integrating OPS5 with UMass GBB

The requirements of a problem-solving architecture that can be tightly embedded within other architectures and coexist with multiple instances of itself and of other problem-solvers are discussed. The additional effort needed to produce an embedable problem-solving architecture is minor compared to the substantial increase in applicability of the architecture. A specific need for embedable problem-solvers arose with the University of Massachusetts Generic Blackboard Framework (UMass GBB). UMass GBB is based on the blackboard paradigm, which naturally integrates heterogeneous problem-solving representations as individual knowledge sources (KSs). This need is met by developing general specifications for embedable problem-solving architectures, and the specifications are used to modify the public-domain version of OPS5 in order to embed it as an integral KS language within UMass GBB. The OPS5 modifications result in an easily integrated GBB KS language (distributed with the UMass GBB system) that has been used in several GBB applications. >

An Ensemble Architecture for Learning Complex Problem-Solving Techniques from Demonstration

We present a novel ensemble architecture for learning problem-solving techniques from a very small number of expert solutions and demonstrate its effectiveness in a complex real-world domain. The key feature of our “Generalized Integrated Learning Architecture” (GILA) is a set of heterogeneous independent learning and reasoning (ILR) components, coordinated by a central meta-reasoning executive (MRE). The ILRs are weakly coupled in the sense that all coordination during learning and performance happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During performance, each ILR proposes partial solutions to subproblems posed by the MRE, which are then selected from and pieced together by the MRE to produce a complete solution. The heterogeneity of the learner-reasoners allows both learning and problem solving to be more effective because their abilities and biases are complementary and synergistic. We describe the application of this novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspaces need to be deconflicted, reconciled, and managed automatically. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Furthermore, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.

Mixed-initiative management of dynamic business processes

Managing and participating in complex, dynamic business processes is difficult due to their inherent uncertainty, which undermines the predictability necessary for efficient planning and execution. Effective management of these processes hinges on the ability of the manager to recognize unanticipated difficulties in the process execution, determine the causes of the anomalies, and implement remedies. Current process-management approaches respond reactively to process dynamics, if they deal with them at all. In this paper, we present the ProME process-management environment, focusing on how human process managers and participants interact with a dynamic, online model of executing dynamic processes to proactively manage and operate in dynamic business processes. We show how having the best information available about a process and its future can provide managers with the time needed to detect and understand impending process anomalies and to develop and implement effective interventions. Furthermore, we show enabling managers how to update the model of executing processes and having the effects of those modifications to be pushed to the relevant participants reduces the time it takes to implement remedies, ProME was used in a commercial product for managing design processes in the automotive and aerospace industries.