Daniel D. Corkill: On-Line Publications

• Leveraging Failures to Enhance Hierarchical Concept Learning when Training and Testing are Limited, Huzaifa Zafar and Daniel D. Corkill. Technical Report UM-CS-2011-026, Department of Computer Science, University of Massachusetts Amherst, July 2011.

  Hierarchical concept learning constructs higher-level concepts using previously learned prerequisite concepts. In our DARPA Bootstrapped Learning program work, we faced an especially challenging context where only a small number of training instances for each concept were provided to the learning system. Such limited instruction forces even the most skillful learner to make assumptions about the concept being taught—assumptions that can be incorrect. Given this uncertainty, multiple candidates may be proposed by learning algorithms for the concept, each stemming from different assumptions that are consistent with the training. We developed a novel control strategy for managing the use of hypothesized concept candidates in higher-level learning. This Concept Candidate Management (CCM) strategy is based on three key ideas: 1) limiting prerequisite-candidate combinatorics by operating with a single selected candidate for each concept at any time, 2) using learning failure to select a different candidate for a direct or indirect prerequisite concept, and 3) using differences observed as candidates are used to guide candidate selection. We evaluated CCM in MABLE, an electronic student that performs bootstrapped concept learning. Using the CCM strategy, MABLE learned concepts that were not successfully learned otherwise—without any additional training or testing and without any changes to learning algorithms. (PDF)

• Deploying Power-Aware, Wireless Sensor Agents, Daniel D. Corkill. The Computer Journal, 54(8):392–405, March 2011.

  Developing sensor agents that can be deployed untethered in the field presents significant challenges in adapting to hardware, communication, power, and environmental limitations. Real-world characteristics dictate agent behavior and operating strategies, sometimes quite differently from often held assumptions and intuitions. In this article, we describe the sensor-agent hardware and blackboard-system software used in CNAS (Collaborative Network for Atmospheric Sensing), an agent-based, power-aware sensor network for ground-level atmospheric monitoring. CNAS is representative of a class of battery-powered, wireless sensor networks in which the distance separating deployed sensor agents is near the limit of their WiFi communication range. To conserve battery power, CNAS sensor agents must have their wireless radios turned off most of the time, as even having them turned on consumes significant power. We discuss how CNAS agents collaborate using only periodic radio availability and consider how different hardware and communication capabilities would change CNAS strategies. We also relate challenges that had to be addressed during deployments of CNAS at military exercises held in the summer heat in Wisconsin and in the rain and mud in Queensland, Australia. We conclude with research on improving CNAS responsiveness with limited radio availability and on potential next-generation CNAS hardware. (PDF)

• Reducing Online Model Development Time by Agents using Constraints between Shared Observations, Huzaifa Zafar and Daniel D. Corkill. The Computer Journal, 53(8):1302–1314, October 2010.

  A situated agent must determine aspects of its environment in order to make appropriate decisions. This determination must be done quickly, as performance can suffer until each agent develops a sufficiently accurate model of its environment. We introduce a two phase model-development approach that leads to a significant reduction in the online (post-deployment) time required to determine environmental models. During the pre-deployment phase, an incompletely specified, site-independent model of an agent's environment is developed, with the site-dependent features represented as parameters in the model. This pre-deployment model is then completed during the post-deployment phase by determining the model parameters using constraints between local and shared observations. In this article, we use this approach in developing an environmental model for potential solar visibility and panel collection characteristics by each agent in a power-aware wireless sensor network. We show that, by using temporal and spatial constraints between shared observations, each agents can complete its solar-harvesting model using only the first and second day observations as compared to 10 days of observations required by the power-management algorithm of Kansal et al. (PDF)

• Agent Technologies for Sensor Networks, Alex Rogers, Daniel D. Corkill, and Nicholas R. Jennings. IEEE Intelligent Systems, 24(2):13–17, March/April 2009.

  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. Such networks consist of small, battery-powered devices that are physically distributed over a wide area and connected through a wireless communication network. Since these networks often must collect data over extended periods of time and are deployed in inhospitable environments where replacing batteries is inconvenient or impossible, much of the research in this domain addresses the challenge of minimizing each sensor's energy needs. To this end, researchers have developed a wide range of energy-efficient sensor nodes and wireless communication protocols and demonstrated them in varied applications. This article describes three example applications. In each case, researchers have demonstrated the work in the wild, implemented it on real sensor hardware, deployed it in real, hostile environments, and evaluated it on real sensor data. (PDF)

• Reporting Down Under: A CNAS (Collaborative Network for Atmospheric Sensing) Update, Daniel D. Corkill. In Second International Workshop on Agent Technology for Sensor Networks (ATSN-08), Estoril, Portugal, pages 25–32, May 2008.

  We briefly review the sensor-agent hardware and blackboard-system software used in CNAS (Collaborative Network for Atmospheric Sensing), an agent-based, power-aware sensor network for ground-level atmospheric monitoring. We then describe experiences and lessons learned from field deployments of CNAS at the 2007 Talisman-Saber Combined Exercise held in Queensland, Australia. We conclude with an overview of CNAS research performed since Talisman-Saber that focuses on: 1) improving CNAS performance and responsiveness with limited radio availability, 2) power-aware reasoning associated with solar harvesting obtained from a rollable solar panel at each sensor agent, and 3) potential next-generation CNAS hardware. (PDF)

• Determining Confidence When Integrating Contributions from Multiple Agents, Raphen Becker and Daniel D. Corkill. In Sixth International Joint Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2007), Honolulu, Hawaii, pages 449–456, May 2007.

  Integrating contributions received from other agents is an essential activity in multi-agent systems (MASs). Not only must related contributions be integrated together, but the confidence in each integrated contribution must be determined. In this paper we look specifically at the issue of confidence determination and its effect on developing “principled,” highly collaborating MASs. A domain-independent analysis model is presented that can be used to measure the sensitivity of a collaborative problem-solving system to potentially incorrect confidence-integration assumptions. The analysis model can be used to determine confidence bounds on integrated contributions and to identify where efforts to improve contribution-dependency estimates lead to the greatest improvement in solution-confidence accuracy. (PDF)

• Turn Off Your Radios! Environmental Monitoring Using Power-Constrained Sensor Agents, Daniel D. Corkill, Douglas Holzhauer, and Walter Koziarz. In First International Workshop on Agent Technology for Sensor Networks (ATSN-07), Honolulu, Hawaii, pages 31–38, May 2007.

  CNAS (Collaborative Network for Atmospheric Sensing) is an agent-based, power-aware sensor network for ground-level atmospheric monitoring. In many multi-agent applications, reducing message transmission is a primary objective. In CNAS, however, it's not the cost of sending messages, but when messages can be sent that is the driving communication constraint. CNAS agents must have their radios turned off most of the time, as even listening consumes significant power. CNAS requires agent policies that can intelligently meet operational requirements while communicating only during intermittent, mutually established, communication windows. CNAS agents and their hardware and blackboard-system software architectures are described, as well as experiences and lessons learned from a field deployment of CNAS at the 2006 PATRIOT Exercise held in July 2006 at Fort McCoy, Wisconsin. (PDF)

• Representation and Contribution-Integration Challenges in Collaborative Situation Assessment, Daniel D. Corkill. In Proceedings of the Eighth International Conference on Information Fusion (Fusion 2005), Philadelphia, Pennsylvania, pages xxix–xxxi, July 2005.

  Blackboard systems are an ideal architecture for situation assessment involving large data volumes and heterogeneous data and knowledge sources. However, the ad hoc confidence and belief values used in traditional blackboard applications has led to criticism of the blackboard approach and spawned efforts to combine collaborative blackboard-system techniques with more “principled” graphical-network representations. Two important collaborative-assessment challenge areas are discussed in this brief position paper: 1) principled blackboard representations and 2) principled integration of contributions made by independent knowledge-source entities. The complexity of these challenges is highlighted using a very simple assessment scenario. (PDF)

• Collaborating Software: Blackboard and Multi-Agent Systems & the Future, Daniel D. Corkill. In Proceedings of the International Lisp Conference, New York, New York, October 2003.

  AI researchers have used the paradigm of collaborating software systems to tackle large and difficult problems. This invited presentation compares and contrasts two markedly different collaborating-software approaches: blackboard systems and multi-agent systems. Examining collaborating software from both perspectives provides insights into the nature of collaboration, reveals unresolved problems in integrating disparate contributions, and underscores issues in coordinating collaborative activities. (PDF)

• Mixed-Initiative Management of Dynamic Business Processes, Zachary B. Rubinstein and Daniel D. Corkill. Proceedings of the 2003 IEEE International Workshop on Soft Computing in Industrial Applications, pages 39–44, Binghamton, New York, June 2003.

  Describes the ProME process-management environment, focusing on how human process managers and participants interact with a dynamic, on-line model of executing dynamic processes to proactively manage and operate in dynamic business processes. Having the best information available about a process and its future provides managers with the time needed to detect and understand impending process anomalies and to develop and implement effective interventions. Furthermore, enabling managers to update the executing process representation and having the ProME environment push the effects of those modifications 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. (PDF)

• Live-Representation Process Management, Daniel D. Corkill, Zachary B. Rubinstein, Susan E. Lander, and Victor R. Lesser. Proceedings of the 5th International Conference on Enterprise Information Systems, pages 203–208, Angers, France, April 2003.

  Describes the "live-representation" approach to managing and working in complex, dynamic business processes. In this promising new approach, important aspects of business-process modeling, project planning, project management, resource scheduling, process automation, execution, and reporting are integrated into a detailed, on-line representation of planned and executing processes. (PDF)

• When Workflow Doesn't Work: Issues in managing dynamic processes, Daniel D. Corkill. Proceedings of the Design Project Support using Process Models Workshop, Sixth International Conference on Artificial Intelligence in Design, Worcester, Massachusetts, June 2000.

  Explains why traditional process-execution systems cannot address the management requirements of dynamic design processes and describes a new type of decision-support software developed specifically to address the management of dynamic design processes. (PDF)

• Diversity in Agent Organizations, Daniel D. Corkill and Susan E. Lander.

  A concise and accessible presentation of the issues associated with agent-based organizations. This is the full version of the article "Agent Organizations" that was published in Object Magazine, 8(4)41-47, May 1998. (HTML)

• Countdown to Success: Dynamic objects, GBB, and RADARSAT-1, Daniel D. Corkill. Communications of the ACM, 40(5):848-858, May 1997.

  An invited account of the importance that blackboard-system and dynamic-object capabilities played in the rapid development of the ground-based portion of the RADARSAT-1 Mission Control System. (PDF)

• Designing Integrated Engineering Environments: Blackboard-Based Integration of Design and Analysis Tools, Susan E. Lander, Scott M. Staley, and Daniel D. Corkill. Concurrent Engineering: Research and Applications, Special Issue on the Application of Multi-Agent Systems to Concurrent Engineering, 4(1):59-72, March 1996.

  Describes the use of blackboard technology in creating the agent-based RRM integrated concurrent-engineering environment for automotive design at Ford. (PostScript)

• Blackboard Systems, Daniel D. Corkill.

  An introduction to blackboard systems. This article discusses the characteristics and potential of blackboard systems. It describes what a blackboard system is and what it is not, considerations for using blackboard systems, and how to get started. This is the unabridged version of the article that appeared in AI Expert 6(9):40-47, September 1991. (PDF)

• Embedable Problem-Solving Architectures: A Study of Integrating OPS5 with UMass GBB, Daniel D. Corkill. IEEE Transactions on Knowledge and Data Engineering 3(1):18-24, March 1991.

  Discusses the issues involved in creating a problem-solving architecture that can be tightly embedded within other architectures and coexist with multiple instances of itself and of other problem-solvers. An detailed example describing modifications and enhancements made to the public-domain version of OPS5 in order to embed it as an integral KS language within the UMass GBB system is presented. (PDF)

• Design Alternatives for Parallel and Distributed Blackboard Systems, Daniel D. Corkill. In V. Jagannathan, Rajendra Dodhiawala, and Lawrence S. Baum, editors, Blackboard Architectures and Applications, pages 99–136. Academic Press, 1989.

  This book chapter discusses important issues in the design of parallel and distributed blackboard architectures, focusing on issues of performance and the maintenance of semantic consistency of the blackboard. (PDF)

• Trends in Cooperative Distributed Problem Solving, Edmund H. Durfee, Victor R. Lesser, and Daniel D. Corkill. IEEE Transactions on Knowledge and Data Engineering, 1(1):63-82, March, 1989 (Invited paper).

  A invited survey of Cooperative Distributed Problem Solving circa 1989. (PostScript)

• Achieving Flexibility, Efficiency, and Generality in Blackboard Architectures, Daniel D. Corkill, Kevin Q. Gallagher, and Philip M. Johnson. In Proceedings of the National Conference on Artificial Intelligence (AAAI-87), pages 18–23, Seattle, Washington, July 1987. (Also published in Readings in Distributed Artificial Intelligence, Alan H. Bond and Les Gasser, editors, pages 451–456, Morgan Kaufmann, 1988.)

  A discussion of the high-performance, dimensional data-abstraction techniques developed for the UMass GBB blackboard-development system. (PDF)

• Use of Meta-Level Control for Coordination in a Distributed Problem-Solving Network, Daniel D. Corkill and Victor R. Lesser. Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI-83), pages 748–756, Karlsruhe, Federal Republic of Germany, August 1983.

  This summary of Corkill's dissertation research describes the first research to specifically explore the use of organizational self-design and coordination in multi-agent systems. Also discussed is the importance of agent skepticism where agents actively monitor the internal conflict between organizationally specified activities and locally desirable actions. An implementation of these ideas is briefly described along with the results of preliminary experiments with various network problem-solving strategies specified via organizational structuring. (PDF)

• Unifying Data-Directed and Goal-Directed Control: An example and experiments, Daniel D. Corkill, Victor R. Lesser, and Eva Hudlicka. Proceedings of the National Conference on Artificial Intelligence (AAAI-82), pages 143-147, Pittsburgh, Pennsylvania, August 1982.

  A description of a blackboard architecture that integrates data-directed and goal-directed control into a single, uniform framework. This architecture was used in the UMass Distributed Vehicle Monitoring Testbed (PDF)

• Functionally Accurate, Cooperative Distributed Systems, Victor R. Lesser and Daniel D. Corkill. IEEE Transactions on Systems, Man, and Cybernetics, SMC-11(1):81-96, January 1981.

  Original presentation of the idea that knowledge-based AI techniques could be used to design distributed systems that could operate with local views that were not complete, consistent, and up to date. (PDF)

• Hierarchical Planning in a Distributed Problem-Solving Environment, Daniel D. Corkill.

  Describes extensions to Sacerdoti's NOAH hierarchical planning system for use with multiple, distributed planning agents. This the full version of the paper that appeared in the Proceedings of the Sixth International Joint Conference on Artificial Intelligence, pages 168-175, Tokyo, August 1979. (HTML)

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• Curriculum Vitae (PDF)