At the Boundary of Workflow and AI

Many domains of interest to the workflow community are characterized by ever-changing requirements and unpredictable environments. Workflow systems must increase in sophistication to provide the reactivity and flexibility n ecessary for process management under such dynamic conditions. This paper describes how techniques from the AI community, specifically reactive control, planning, and scheduling, could be leveraged to develop powerful, next-generation adaptive workflow engines that provide many of the these advanced process management capabilities. Although motivated by somewhat different concerns and grounded in different perspectives, there is much overlap between the objectives and requirements of these two communities of workflow management and Artificial Intelligence. Two systems under development by the authors which embrace this synergy are described.

[1]  Victor R. Lesser,et al.  The Distributed Vehicle Monitoring Testbed: A Tool for Investigating Distributed Problem Solving Networks , 1983, AI Mag..

[2]  David E. Wilkins,et al.  Recovering from execution errors in SIPE , 1985, Comput. Intell..

[3]  Terry Winograd,et al.  Understanding computers and cognition , 1986 .

[4]  Kristian J. Hammond,et al.  Explaining and Repairing Plans that Fail , 1987, IJCAI.

[5]  David E. Wilkins,et al.  Practical planning - extending the classical AI planning paradigm , 1989, Morgan Kaufmann series in representation and reasoning.

[6]  Alan H. Bond,et al.  Distributed Artificial Intelligence , 1988 .

[7]  Kristian J. Hammond,et al.  Case-Based Planning: Viewing Planning as a Memory Task , 1989 .

[8]  Jeffrey S. Rosenschein,et al.  Negotiation and Task Sharing Among Autonomous Agents in Cooperative Domains , 1989, IJCAI.

[9]  Michael P. Georgeff,et al.  Decision-Making in an Embedded Reasoning System , 1989, IJCAI.

[10]  Mark Drummond,et al.  Situated Control Rules , 1989, KR.

[11]  Johannes Klein,et al.  Coordinating multi-transaction activities , 1990 .

[12]  Patrice Godefroid,et al.  An Efficient Reactive Planner for Synthesizing Reactive Plans , 1991, AAAI.

[13]  Paul R. Cohen,et al.  Failure Recovery: A Model and Experiments , 1991, AAAI.

[14]  Pauline M. Berry,et al.  The PCP: A predictive model for satisfying conflicting objectives in scheduling problems , 1992, Artif. Intell. Eng..

[15]  Manuela Veloso.  Learning by analogical reasoning in general problem-solving , 1992 .

[16]  James A. Hendler,et al.  A Validation-Structure-Based Theory of Plan Modification and Reuse , 1992, Artif. Intell..

[17]  John L. Bresina,et al.  Reaction-First Search , 1993, IJCAI.

[18]  David J. Musliner,et al.  CIRCA: a cooperative intelligent real-time control architecture , 1993, IEEE Trans. Syst. Man Cybern..

[19]  Monte Zweben,et al.  Scheduling and rescheduling with iterative repair , 1993, IEEE Trans. Syst. Man Cybern..

[20]  R. James Firby,et al.  Task Networks for Controlling Continuous Processes , 1994, AIPS.

[21]  Dana S. Nau,et al.  Semantics for hierarchical task-network planning , 1994 .

[22]  D. Hollingsworth.  The workflow Reference Model , 1994 .

[23]  Adele E. Howe,et al.  Improving the Reliability of Artificial Intelligence Planning Systems by Analyzing their Failure Recovery , 1995, IEEE Trans. Knowl. Data Eng..

[24]  A. El Abbadi,et al.  Exotica: a project on advanced transaction management and workflow systems , 1995, SIGO.

[25]  David J. Musliner,et al.  World Modeling for the Dynamic Construction of Real-Time Control Plans , 1995, Artif. Intell..

[26]  Donald E. Brown,et al.  Intelligent Scheduling Systems , 1995 .

[27]  Johann Eder,et al.  The Workflow Activity Model WAMO , 1995, CoopIS.

[28]  Stephen F. Smith,et al.  Reactive Scheduling Systems , 1995 .

[29]  Karen L. Myers.  A Procedural Knowledge Approach to Task-Level Control , 1996, AIPS.

[30]  Karen L. Myers.  Strategic Advice for Hierarchical Planners , 1996, KR.

[31]  Amit P. Sheth,et al.  Scheduling workflows by enforcing intertask dependencies , 1996, Distributed Syst. Eng..

[32]  Edward P. K. Tsang,et al.  Adaptive Constraint Satisfaction: The Quickest First Principle , 1996, ECAI.

[33]  Gregg Collins,et al.  2Planning for Contingencies: A Decision-based Approach , 1996, J. Artif. Intell. Res..

[34]  Katia Sycara,et al.  Multiagent coordination in tightly coupled task scheduling , 1997 .

[35]  Craig A. Knoblock,et al.  Planning by Rewriting: E ciently Generating High-Quality Plans , 1999 .

[36]  Andrzej Cichocki,et al.  Workflow and Process Automation: Concepts and Technology , 1997 .

[37]  Munindar P. Singh,et al.  Readings in agents , 1997 .

[38]  David E. Wilkins,et al.  A Multiagent Planning Architecture , 1998, AIPS.

[39]  Manuela M. Veloso,et al.  Rationale-Based Monitoring for Planning in Dynamic Environments , 1998, AIPS.

[40]  Mark S. Fox,et al.  Intelligent Scheduling , 1998 .

[41]  David Joslin,et al.  "Squeaky Wheel" Optimization , 1998, AAAI/IAAI.

[42]  Andrzej Cichocki,et al.  Workflow and Process Automation , 1998 .

[43]  Thomas J. Lee,et al.  THE AIR CAMPAIGN PLANNING KNOWLEDGE BASE , 1999 .

[44]  Karen L. Myers.  Towards a Framework for Continuous Planning and Execution , 2000 .