Challenges and Opportunities for Innovation in the Public Works ...

Sharing Experience Between Manufacturing and Construction
learning from AIMS for acquisition of probabilistic values to support design and manufacturing
integration. The system supports what-if analysis (R-c-4) and the learning tasks can be
distributed across networked computers (R-d-4).
NEXT-IDEEA Advanced Versions of IDEEA: many major extensions are being developed for
IDEEA to enhance its basic capabilities as a more robust and useful tool for concurrent product
development. These extensions include integration with CAD tool for geometry processing (Ra-4),
recording of decision rationale (R-b-1), domain dependent explanations (R-b-4) at multiple
levels of details (R-c-2), hypothetical reasoning (R-c-4), and a blackboard architecture for group
problem solving (R-d-).
CYCLE Classification and Sorting of Recyclable Containers: an application of AIMS for the
control of continuous operations based on on-line sensory information (36). Although not
directly related to concurrent engineering, the system demonstrates the abilities to integrate
sensors with learning algorithms (R-d-1) to produce classification models with varying details (Rc-2),
both are required functions for concurrent engineering.
NEURAL Neural Networks for Complex Control: an application of neural network learning
algorithms to open-loop controls of complex mechanical systems (e.g., a combine harvester) (37).
The system is able to integrate various sources of knowledge (R-a-1) about the process and adjust
machine control parameters to adapt to varying operation conditions (R-c-2).
HIDER Hierarchical and Interactive Design Refinement: a domain-independent system which
supports hierarchical refinement of decision spaces through successive optimizations with layered
models induced by AIMS (38). This methodology bridges the synthesis and analysis gaps (R-a-2),
supports decisions with varying abstractions (R-c-2), and facilitates early evaluations of designs
(R-c-3). HIDER is being applied to the domains of concurrent engine design and quality control
of semiconductor production.
META-AIMS Meta Learning System for AIMS: a meta-leaming system which learns how to
optimally control and select various learning algorithms in AIMS with different application
domains (39). Such a system can enhance the performance and operation of AIMS in concurrent
engineering applications.
INVERSE Inverse Engineering Methodology: a machine learning system that can automatically
induce knowledge to support design synthesis from those analytical models of the domain (R-a-2)
(40). The models produced are "invertible" and can be at various levels of details (R-c-2)
including those required for early stages (R-c-3).
KBL Knowledge-Based Learning: an integrated machine learning system that combines the
strengths of inductive and deductive algorithms (r-d-1) for real-world problems which are dataintensive
and knowledge-sparse (41). The system can work with various representations of
domain knowledge (R-a-l).
71