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IWLU Panel Panelists: Mary Shaw, Steve Easterbrook, Betty Cheng, David Garlan, Alexander Egyed, Alex...
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Transcript of IWLU Panel Panelists: Mary Shaw, Steve Easterbrook, Betty Cheng, David Garlan, Alexander Egyed, Alex...
IWLU Panel
Panelists: Mary Shaw, Steve Easterbrook, Betty Cheng, David
Garlan, Alexander Egyed, Alex Orso,
Tim Menzies
Moderator: Marsha Chechik
Where is Uncertainty?
• Where are we now (current state)• Which way we are going (process)• Where do we want to end up (product, end
result)• What is the configuration of our system?• What are the properties of our
environment?• What is process scheduling for our
system?• What are sources of our information?
Dealing with Uncertainty(stolen without permission from Steve
Easterbrook)
• Avoid/ignore. Uncertainty can be isolated and is not important
• Resolve – negotiate a new solution or find a compromise
Circumvent – make (some) decisions under some model of uncertainty
• Ameliorate – take actions that improve the situation but which do not get rid of the uncertainty
Quick Summary of Interests of Workshop Participants
• AI/SE border in describing/reasoning with uncertainty– For process and product– Based on probabilities
• Decision making– Including delaying and reconsidering decisions
• Formal methods• Architectural design
– Modeling– Reasoning– Advice to architects
More interests
• Uncertainty in testing• Dynamically adaptive systems:
– Requirements elicitation– Decision making
• Uncertainly/inconsistency in viewpoints• Pervasive computing:
– Resource allocation, QoS management under uncertainty
• Incremental requirements• Impact of early design decisions
Questions to the Panelists• What have I learned today? (or what I believe to be true)
– How do we make decisions in the presence of incompleteness?– What problems are most difficult (for decision making or for
elimination)– Which are most important?– What problems are most feasible to tackle, in the short run and
in the long run
• How do we proceed?– How do we coordinate efforts?– Challenge problems– What do we offer as a baseline?
• …So that other results can improve or refute• And how do we document that?
More Global Questions
• Is there a community forming?– (easier) Should we have another workshop?– (harder) What should be our research
agenda?
imprecise
inaccurate
unclear
confused
context-sensitive
incompleteabstractsubjective
ambiguous
nondeterministic
inconsistentuntrustworthy
low-confidence
optional
evolving
contingent
DETAIL KNOWLEDGE
ENVIRONMENT
FUTURE
SEMANTICS
uncertain (in time)
unreliable
mistrusted
Outcomes
• Initial classification of what incompleteness means• Incompleteness is EVERYWHERE and we should have
had this a long time ago• Decision making requires more completeness
– Economical argument. Who are stakeholders?• Sensitivity analysis. What will help do prediction
• Need to build adaptible systems. Always!• Goal: explain competence in the presence of uncertainty
– Knowledge acquisition: even experts disagree about stuff they know (even with themselves)
– Need to know stochastic theorem proving– Non-monotonic reasoning (model-based diagnosis people)– Need empirical basis (including parameterization of uncertainty)
More Outcomes
• Learn from other fields– Probability/statistics, AI – Inspiration from real world (e.g., snails)– Use Avida to support model evolution
• Need more of:– Interplay between human and computer reasoning– Reasoning takes place in reactive environment and
does by groups of people where noone has complete info.
• Explicit boundaries of decision-making• Realiance on other people (how to scale this up from small
company to large)
More Outcomes
• Autonomous evolution– Using dynamic info to steer the model
• Going from architecture down to code. – Has strong relevance to testing
• Did not hear enough of– What to do if it is impossible to ask the user for more
information!– What is the boundary between incompleteness and uncertainty?– How to reasoning anyway?
• Did not hear enough of– Use of backtracking when wrong decisions were made– When to backtrack?– How far to backtrack
Yet More Outcomes
• Evolution of systems and uncertainty:– From simple programs to human-in-the-loop,
uncertainty grows– Need to explicitly integrate uncertainty and
quantify it• Permitting incremental refinement
Research Questions
• How to decide what additional info is most useful for comparing designs?
• How to explain the context of slack?– Need to plan for incompleteness and build in
excess capacity
• Can we develop the calculus of confidence and other subjective info
• Tools for expertise management– Where to go to for info?
More Research Questions
• How to decide what we need to know and what should be factored into the model?
• How to estimate quality of these models as far as prediction goes
• How to create runtime systems to help adapt?• How to validate our results?• Reiterate: reasoning with uncertainty, when
more information cannot be elicited• Use of backtracking
Yet More Research Questions
• Development of a challenge problem– With a baseline solution
• … or not• … or a model problem?
– Anything as long as there is an objective– And based on real people doing real things
• Maybe begin by defining terms– Survey paper
• Maybe a set of scenarios (will get a link)• Look for principally different models• Law of medium numbers…