Artificial Intelligence for Engineering Design, Analysis and Manufacturing

AIEDAM Special Issue, Summer 2009, Vol. 23, No. 3
Problem Solving Methods: Past, Present and Future
Edited by: David C. Brown

This special issue of AIEDAM will be devoted to invited papers concerned with Problem Solving Methods (PSMs). It will examine whether they have fulfilled their early promise, will examine the difficulties that still remain, and will make predictions about their use in the future development of knowledge-based systems, in particular those built and delivered over the Web.

A partial list of PSM references, including many key papers, can be found at: web.cs.wpi.edu/~dcb/courses/CS538/References07.html

PSMs are highly relevant for AI EDAM and its readers. Knowledge-based computational support for Engineering Design, Analysis and Manufacturing has provided motivation for the study of PSMs, and the area can clearly benefit from them. For example, heuristic classification is at the heart of many selection problems: selecting an appropriate feature to include in a design, picking the right material, using the right analysis tool, or deciding on the processing of a material. In addition, intelligent parametric design, and configuration, are both within the scope of the journal.

For this special issue the authors have been asked to respond to a set of questions and challenges about PSMs. These are given below.

The main questions are:

• Have the original goals of the PSM movement been achieved? e.g., from the Role Limiting Methods (McDermott, 1988) and Generic Tasks (Chandrasekaran & Johnson, 1993) research, for example.
• Is there still good work concerned with studying and developing PSMs, or have PSMs been branded a failure and largely forgotten?
• What is the future of PSMs?

Additional more detailed issues that need discussion include the following:

• Are there any more PSMs? In the PSM work from about 1983 to 2000 there are many lists: Schreiber et al. (1999) list thirteen types of tasks, for example. Are there more to be found? Do we need them?
  
  
• What is an appropriate grain size for PSMs? In the Generic Tasks (GT) line of research (Chandrasekaran and Johnson, 1993) the conclusion was reached that what were earlier thought to be primitive GTs, were actually made up of other finer grained tasks, themselves usful tasks. While analyzing my own work on the Design Specialists and Plans Language (DSPL), I found thirteen ingredient activities, all potentially of general use (Brown, 1992).
  
  
• Does it make sense to open the use of PSMs to a wider range of less controlled reasoning activity, not just the traditional expert tasks? If so, is the question of how many PSMs there are, or their grain size, still relevant? i.e., can we recognize what isn't a PSM?
  
  
• Is it appropriate to consider PSMs as assembled dynamically in response to needs, or retrieved as complete units? Much of the literature on PSMs tends to consider retrieving complete units. While a number of more recent authors acknowledge the need for task-subtask decomposition with the resulting reasoner being an assembly of PSMs, there hasn't been a lot of attention paid to that configuration process.
  
  
• Can complete systems built using PSMs be automagically assembled? Can complete systems built using PSMs be assembled by humans? Is there room for both?
  
  
• Should usability be focussed on more? Most more recent work on PSMs (e.g., Motta, 1999; Fensel, 2000) rely on expressing some of the knowledge (e.g., assumptions and goals) in some form of formal, logical language. Logic is notoriously user-unfriendly for anyone without the right training. While there is certainly room for this approach---what I'm going to refer to as "PSM heavy"---surely it makes sense to have "PSM light" versions available as well? i.e., versions using less intimidating languages, perhaps with less flexibility, or provided as "toolkits" in the original GT style.
  
  
• When faced with a choice of library and a choice of PSMs for the same task within each library, which PSM should be selected from which library? i.e., other issues besides whether it can do the Task, such as speed, space, accuracy, and reliability need to be involved.
  
  
• What are the recent successes of the use of PSMs from PSM libraries? i.e., has all the hype been justified? Why should they be considered as successes, and where have they been documented?
  
  
• What are the recent failures of PSM research? Why should they be considered as failures, and where have they been documented?
  
  
• Are there other directions within AI that the PSM research can lead to? e.g., incorporating PSMs with analogical reasoning, with spatial reasoning, with sensing.
  
  
• Currently the PSM research effort has become strongly tied to the use of Ontologies. Is that a good thing? For example, if ontologies in fact are "use dependent" (as Noy & McGuinness [2001] and others point out) and vary according to use/task, then how many ontologies do you need?
  
  
• Has the latest work on integrating PSM concepts with the Web been 'distracted' by semantic web technology, including ontology engineering? Combining web services and integrating data from different sources raises its own interesting challenges, and has the seductive power of potential worldwide use. However, does the original need for knowledge-based problem-solving systems still remain?
  
  
• There is some doubt about whether the problem of describing and using existing PSMs has been completely solved. If this is the case, why is it appropriate to open the PSM research to the use of even more varied and potentially powerful web services? As this is a harder, more open case, can they be described, discovered and combined properly?
  
  

References:

D.C. Brown (1992) The Reusability of DSPL Systems. Position Statement for the Workshop on Reusable Design Systems, 2nd Int. Conf. AI in Design, Carnegie-Mellon University, Pittsburgh, USA.

B. Chandrasekaran and T.R. Johnson (1993) Generic Tasks And Task Structures: History, Critique and New Directions. Second Generation Expert Systems, J.M. David, J.P. Krivine, and R. Simmons, Eds., Springer-Verlag, pp. 239-280.

D. Fensel (2000) Problem-Solving Methods: Understanding, Description, Development, and Reuse. Lecture Notes in AI (LNAI) 1791, Springer.

J. McDermott (1988) Preliminary steps towards a taxonomy of problem-solving methods. In Automating Knowledge Acquisition for Expert Systems, S. Marcus, Ed., Kluwer Academic Publishers, Boston, pp. 225-256.

E. Motta (1999) Reusable Components for Knowledge Modelling. IOS Press, Amsterdam, The Netherlands.

N.F. Noy & D.L. McGuinness (2001) Ontology Development 101: A Guide to Creating Your First Ontology. http://protege.stanford.edu/publications/ontology_development/ontology101-noy-mcguinness.html, accessed June 8, 2007.

G. Schreiber, H. Akkermans, A. Anjewierden, R. de Hoog, N. Shadbolt, W. Van de Velde, and B. Wielinga (1999) Knowledge Engineering and Management: The CommonKADS Methodology. The MIT Press.