Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-08T00:32:40.182Z Has data issue: false hasContentIssue false
  • English
  • Français

Workflow agents versus expert systems: Problem solving methods in work systems design

Published online by Cambridge University Press:  14 October 2009

William J. Clancey ,
Maarten Sierhuis  and
Chin Seah
William J. Clancey
Affiliation:
NASA Ames Research Center, Moffett Field, California, USA Florida Institute for Human and Machine Cognition, Pensacola, Florida, USA
Maarten Sierhuis
Affiliation:
Carnegie Mellon University Silicon Valley, NASA Ames Research Center, Moffett Field, California, USA
Chin Seah
Affiliation:
Stinger Ghaffarian Technologies, NASA Ames Research Center, Moffett Field, California, USA
Get access Rights & Permissions [Opens in a new window]

Abstract

During the 1980s, a community of artificial intelligence researchers became interested in formalizing problem solving methods (PSMs) as part of an effort called “second-generation expert systems.” We provide an example of how we are applying second-generation expert systems concepts in an agent-based system for space flight operations, the orbital communications adapter mirroring system (OCAMS), which was developed in the Brahms multiagent framework. Brahms modeling language provides an ontology for simulating work practices, including groups, agents, activities, communications, movements, and geographic areas. Activities are a behavioral unit of analysis to be contrasted with tasks, a functional unit of analysis. Problem solving occurs in the context of activities in the service of tasks; appropriate PSMs depend on the context: which people/roles are participating, what tools are available, how the results will be evaluated, and so forth. A work practice simulation facilitates designing workflow tools that appropriately interact with the physical and organizational context in which work occurs. OCAMS was developed using a simulation-to-implementation methodology, in which a prototype workflow tool was embedded in a Brahms simulation of how people would use the tool. The reusable components in a workflow system like OCAMS include entire “problem solvers” (e.g., a planning subsystem), interoperability frameworks, and agents that inspect and change the world. Thus, a tool kit for building workflow tools requires more than a library of PSMs, which play a relatively small role in the overall multiagent, systems-integration architecture. Our research concern has shifted to situations that may arise that are outside the OCAMS' capability. In practical decision making, people must reflect on the validity of their models. As programs becoming actors in the workplace, we need to develop systems that help people to understand the limitations of the models that drive the automated operations, which means in part detecting when the formalizations in the system are inadequate.

Keywords

Model-Based AutomationProblem Solving AgentSituated CognitionWork Practice SimulationWork Systems Design
Type
Research Article
Information
AI EDAM , Volume 23 , Issue 4: Problem Solving Methods: Past, Present, and Future , November 2009 , pp. 357 - 371
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Acquisti, A., Sierhuis, M., Clancey, W.J., & Bradshaw, J.M. (2002). Agent-based modeling of collaboration and work practices onboard the International Space Station. Proc. 11th Computer-Generated Forces and Behavior Representation Conf., pp. 181–188.Google Scholar
Anderson, R.H. (1977, June). The use of production systems in RITA to construct personal computer “agents.” SIGART Newsletter 63, 23–28.Google Scholar
Anderson, R.H., & Gillogly, J.J. (1976). Rand Intelligent Terminal Agent (RITA): Design Philosophy. RAND Report R-1809-ARPA. Washington, DC: Rand Corporation.Google Scholar
Bonasso, P., Firby, J.R., Gat, E., Kortenkamp, D., Miller, D., & Slack, M. (1997). Experiences with an architecture for intelligent, reactive agents. Journal of Experimental Theory of Artificial Intelligence 9, 237–256.Google Scholar
Bond, A.H., & Gasser, L. (1988). Readings in Distributed Artificial Intelligence. San Mateo, CA: Morgan Kaufmann.Google Scholar
Brooks, R.A. (1991). How to build complete creatures rather than isolated cognitive simulators. In Architectures for Intelligence (VanLehn, K., Ed.), pp. 225–239. Hillsdale, NJ: Erlbaum.Google Scholar
Burton, R.R., & Brown, J.S. (1979). An investigation of computer coaching for informal learning activities. International Journal of Man–Machine Studies 11(1), 5–24.Google Scholar
Carley, K. (1990). Group stability: a socio-cognitive approach. In Advances in Group Processes, Advances in Group Processes: Theory and Research (Lawler, E.J., Markovsky, B., Ridgeway, C., & Walker, H.A., Eds.), Vol. 7, pp. 1–44. Greenwich, CT: JAI Press.Google Scholar
Chandrasekaran, B., & Johnson, T.R. (1993). Generic tasks and task structures: history, critique and new directions. In Second Generation Expert Systems (David, J.M., Krivine, J.P., & Simmons, R., Eds.), pp. 239–280. New York: Springer–Verlag.Google Scholar
Choo, T.H., & Skura, J.P. (2004). SciBox: a software library for rapid development of science operation simulation, planning, and command tools. Johns Hopkins APL Technical Digest 25(2), 154–162.Google Scholar
Clancey, W.J. (1984). Methodology for building an intelligent tutoring system. In Method and Tactics in Cognitive Science (Kintsch, W., Miller, J.R., & Polson, P.G., Eds.), pp. 51–83. Hillsdale, NJ: Erlbaum.Google Scholar
Clancey, W.J. (1985). Heuristic classification. Artificial Intelligence 27, 289–350.Google Scholar
Clancey, W.J. (1986). Qualitative student models. In Annual Review of Computer Science, pp. 381–450. Palo Alto: Annual Reviews.Google Scholar
Clancey, W.J. (1989). Viewing knowledge bases as qualitative models. IEEE Expert: Intelligent Systems and Their Applications 4(2), 9–15, 18–23.Google Scholar
Clancey, W.J. (1992). Model construction operators. Artificial Intelligence 53(1), 1–124.Google Scholar
Clancey, W.J. (1997 a). Situated Cognition: On Human Knowledge and Computer Representations. New York: Cambridge University Press.Google Scholar
Clancey, W.J. (1997 b). The conceptual nature of knowledge, situations, and activity. In Human and Machine Expertise in Context (Feltovich, P., Ford, K., & Hoffman, R., Eds.), pp. 247–291. Menlo Park, CA: AAAI Press.Google Scholar
Clancey, W.J. (1999). Conceptual Coordination: How the Mind Orders Experience in Time. Hillsdale, NJ: Erlbaum.Google Scholar
Clancey, W.J. (2002). Simulating activities: relating motives, deliberation, and attentive coordination. Cognitive Systems Research 3(3), 471–499.Google Scholar
Clancey, W.J. (2004). Roles for agent assistants in field science: personal projects and collaboration. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 34(2), 125–137.Google Scholar
Clancey, W.J. (2005). Towards on-line services based on a holistic analysis of human activities. In Towards the Learning GRID: Advances in Human Learning Services (Ritrovato, P., Allison, C., Cerri, S.A., Dimitrakos, T., Gaeta, M., & Salerno, S., Eds.), pp. 3–11. Frontiers in Artificial Intelligence and Applications, Amsterdam: IOS Press.Google Scholar
Clancey, W.J. (2006). Observation of work practices in natural settings. In Cambridge Handbook on Expertise and Expert Performance (Ericsson, A., Charness, N., Feltovich, P., & Hoffman, R., Eds.), pp. 127–145. New York: Cambridge University Press.Google Scholar
Clancey, W.J. (2008). Scientific antecedents of situated cognition. In Cambridge Handbook of Situated Cognition (Robbins, P., & Aydede, M., Eds.), pp. 11–34. New York: Cambridge University Press.Google Scholar
Clancey, W.J., & Barbanson, M. (1991). TOPO: implications of the system–model–operator metaphor for knowledge acquisition. IEEE Expert 6(5), 61–65.Google Scholar
Clancey, W.J., & Letsinger, R. (1981). NEOMYCIN: reconfiguring a rule-based expert system for application to teaching. Proc. 7th IJCAI, pp. 829–826.Google Scholar
Clancey, W.J., Sachs, P., Sierhuis, M., & van Hoof, R. (1998). Brahms: simulating practice for work systems design. International Journal of Human–Computer Studies 49, 831–865.Google Scholar
Clancey, W.J., Sierhuis, M., Alena, R., Berrios, D., Dowding, J., Graham, J.S., Tyree, K.S., Hirsh, R.L., Garry, W.B., Semple, A., Buckingham Shum, S.J., Shadbolt, N., & Rupert, S. (2005). Automating CapCom using mobile agents and robotic assistants. American Institute of Aeronautics and Astronautics 1st Space Exploration Conf. NASA Report TP 2007-214554. Accessed at http://ntrs.nasa.govGoogle Scholar
Clancey, W.J., Sierhuis, M., Damer, B., & Brodsky, B. (2005). The cognitive modeling of social behavior. In Cognitive Modeling and Multi-Agent Interaction (Sun, R., Ed.), pp. 151–184. New York: Cambridge University Press.Google Scholar
Clancey, W.J., Sierhuis, M., Seah, C., Buckley, C., Reynolds, F., Hall, T., & Scott, M. (2008). Multi-agent simulation to implementation: a practical engineering methodology for designing space flight operations. In Engineering Societies in the Agents' World VIII. Lecture Notes in Artificial Intelligence (Artikis, A., O'Hare, G., Stathis, K., & Vouros, G., Eds.), Vol. 4995, pp. 108–123. Heidelberg: Springer.Google Scholar
Clancey, W.J., Torok, D.M., Sierhuis, M., Hoof, R.J.J.V., & Sachs, P. (2001). Simulating work behavior. US Patent 6,216,098.Google Scholar
Cohen, P.R., Greenberg, M.L., Hart, D.M., & Howe, A.E. (1989). Trial by fire: understanding the design requirements for agents in complex environments. AI Magazine 10(3), 34–48.Google Scholar
Columbia Accident Investigation Board (2003). CAIB Report, Vol. 1. NASA. Accessed at http://www.caib.us/news/report/volume1/default.htmlGoogle Scholar
David, J.M., Krivine, J.P., & Simmons, R., Eds. (1993). Second Generation Expert Systems. New York: Springer–Verlag.Google Scholar
Dourish, P., & Button, G. (1998). On “technomethodology”: foundational relationships between ethnomethodology and system design. Human–Computer Interaction 13, 395–432.Google Scholar
Feltovich, P.J., Bradshaw, J.M., Clancey, W.J., Johnson, M., & Bunch, L. (2007). Progress appraisal as a challenging element of coordination in human and machine joint activity. Proc. ESAW 2007, pp. 124–141.Google Scholar
Frank, J., Morris, P.H., Green, J., & Hall, T. (2008). The challenge of evolving mission operations tools for manned spaceflight. Proc. 9th iSAIRAS 2008.Google Scholar
Gewin, V. (2008). The new networking nexus. Nature 451, 1024–1025.Google Scholar
Gilbert, N., & Doran, J. (1993). Simulating Societies: The Computer Simulation of Social Phenomena. London: UCL Press.Google Scholar
Greenbaum, J., & Kyng, M., Eds. (1991). Design at Work: Cooperative Design of Computer Systems. Hillsdale, NJ: Erlbaum.Google Scholar
Hayes-Roth, F., Waterman, D.A., & Lenat, D., Eds. (1983). Building Expert Systems. Reading, MA: Addison–Wesley.Google Scholar
Hendler, J. (2009). The Semantic Web from the bottom up. In Switching Codes: “Ontology, Induction, and Semantic Web” (Bartscherer, T., & Coover, R., Eds.). Chicago: University of Chicago Press.Google Scholar
Hutchins, E. (1995). Cognition in the Wild. Cambridge: MIT Press.Google Scholar
Latour, B. (1991). Technology is society made durable. In A Sociology of Monsters: Essays on Power, Technology, and Domination (Law, J., Ed.), pp. 103–131. New York: Routledge.Google Scholar
McCurdy, M., Pyrzak, G., Ratterman, C., & Vera, A. (2006). The design of efficient ground software tools. Proc. 2nd IEEE Int. Conf. Space Mission Challenges for Information Technology, p. 257.Google Scholar
McDermott, J. (1988). Preliminary steps toward a taxonomy of problem-solving methods. In Automating Knowledge Acquisition for Expert Systems (Marcus, S., Ed.), pp. 225–256. Boston: Kluwer Academic.Google Scholar
NASA (1980). Machine Intelligence and Robotics: Report of the NASA Study Group. Office of Aeronautics and Space Technology. Accessed at http://www.ntis.gov/ and http://www.rr.cs.cmu.edu/NASA.pdfGoogle Scholar
Newell, A., & Simon, H.A. (1972). Human Problem Solving. Englewood Cliffs, NJ: Prentice–Hall.Google Scholar
Nii, H.P. (1986 a). Blackboard systems. AI Magazine 7(2), 38–53.Google Scholar
Nii, H.P. (1986 b). Blackboard systems. AI Magazine 7(3), 82–106.Google Scholar
Robbins, P., & Aydede, M., Eds. (2008). The Cambridge Handbook of Situated Cognition. New York: Cambridge University Press.Google Scholar
Rutledge, G.W., Thomsen, G.E., Farr, B.R., Tovar, M.A., Polaschek, J.X., Beinlich, I.A., Sheiner, L.B., & Fagan, L.M. (1992). The Design and Implementation of a Ventilator-Management Advisor. Stanford Knowledge Systems Laboratory. Accessed at ftp://ksl.stanford.edu/pub/KSL_Reports/./KSL-92-11.ps.gzGoogle Scholar
Schön, D.A. (1987). Educating the Reflective Practitioner: Toward a New Design for Teaching and Learning in Professions. San Francisco, CA: Jossey–Bass.Google Scholar
Schreiber, A.T., Wielinga, B.J., de Hoog, R., Akkermans, J.M., & Van de Velde, W. (1994). CommonKADS: a comprehensive methodology for KBS development. IEEE Expert 9(6), 28–37.Google Scholar
Seah, C., Sierhuis, M., & Clancey, W.J. (2005). Multi-agent modeling and simulation approach for design and analysis of MER mission operations. Proc. Int. Conf. Human–Computer Interface Advances for Modeling and Simulation, pp. 73–78.Google Scholar
Searle, R. (1969). Speech Acts: An Essay in Philosophy of Language. New York: Cambridge University Press.Google Scholar
Shalin, V.L. (2005). The roles of humans and computers in distributed planning for dynamic domains. Cognition, Technology, and Work 7(3), 198–211.Google Scholar
Sierhuis, M. (2001). Modeling and simulating work practice. PhD Thesis, University of Amsterdam.Google Scholar
Sierhuis, M., Clancey, W.J., Seah, C., Trimble, J., & Sims, M.H. (2003). Modeling and simulation for mission operations work systems design. Journal of Management Information Systems 19(4), 85–128.Google Scholar
Sierhuis, M., Clancey, W.J., & van Hoof, R. (2007). Brahms: a multiagent modeling environment for simulating work practice in organizations. International Journal for Simulation and Process Modeling 3(3), 134–152.Google Scholar
Sierhuis, M., Clancey, W.J., & van Hoof, R. (2009). Brahms: an agent-oriented language for work practice simulation and multi-agent systems development. In Multi-Agent Programming: Languages, Tools and Applications (Bordini, R.H., Dastani, M., Dix, J., & El Fallah Seghrouchni, A., Eds.). New York: Springer.Google Scholar
Simon, H.A. (1973). The structure of ill-structured problems. Artificial Intelligence 4(3), 181–202.Google Scholar
Tufte, E. (2006). Beautiful Evidence. Cheshire, CT: Graphics Press.Google Scholar
Vicente, K.J. (1999). Cognitive Work Analysis: Toward Safe, Productive, and Healthy Computer-Based Work. Mahwah, NJ: Erlbaum.Google Scholar
Wallace, B., & Ross, A. (2006). Beyond Human Error: Taxonomies and Safety Science. Boca Raton, FL: CRC Press.Google Scholar
Wallace, B., Ross, A., Davies, J.B., & Anderson, T., Eds. (2007). The Mind, the Body and the World: Psychology After Cognitivism. London: Imprint Academic.Google Scholar
Wynn, E. (1991). Taking practice seriously. In Design at Work: Cooperative Design of Computer Systems (Greenbaum, J., & Kyng, M., Eds.), pp. 45–64. Hillsdale, NJ: Erlbaum.Google Scholar