112 research outputs found
Filtering Algorithms for the Multiset Ordering Constraint
Constraint programming (CP) has been used with great success to tackle a wide
variety of constraint satisfaction problems which are computationally
intractable in general. Global constraints are one of the important factors
behind the success of CP. In this paper, we study a new global constraint, the
multiset ordering constraint, which is shown to be useful in symmetry breaking
and searching for leximin optimal solutions in CP. We propose efficient and
effective filtering algorithms for propagating this global constraint. We show
that the algorithms are sound and complete and we discuss possible extensions.
We also consider alternative propagation methods based on existing constraints
in CP toolkits. Our experimental results on a number of benchmark problems
demonstrate that propagating the multiset ordering constraint via a dedicated
algorithm can be very beneficial
Governing AI safety through independent audits
Highly automated systems are becoming omnipresent. They range in function from self-driving vehicles to advanced medical diagnostics and afford many benefits. However, there are assurance challenges that have become increasingly visible in high-profile crashes and incidents. Governance of such systems is critical to garner widespread public trust. Governance principles have been previously proposed offering aspirational guidance to automated system developers; however, their implementation is often impractical given the excessive costs and processes required to enact and then enforce the principles. This Perspective, authored by an international and multidisciplinary team across government organizations, industry and academia, proposes a mechanism to drive widespread assurance of highly automated systems: independent audit. As proposed, independent audit of AI systems would embody three ‘AAA’ governance principles of prospective risk Assessments, operation Audit trails and system Adherence to jurisdictional requirements. Independent audit of AI systems serves as a pragmatic approach to an otherwise burdensome and unenforceable assurance challenge
DIDS: rapidly prototyping configuration design systems
The domain independent design system (DIDS) provides a set of tools for rapidly constructing new configuration design systems from a library of reusable software elements called mechanisms . A DIDS user begins by creating a model of the problem domain and the task to be automated. This includes describing a library of parts from which new artifacts could be configured, optimization and preference criteria, and functionality constraints. DIDS analyzes this input and automatically builds an operational prototype system by selecting and combining mechanisms. DIDS' ability to automate this process is derived from its model of configuration design, which enables reusable mechanisms to be identified and automatically selected based on a problem's characteristics. The use of DIDS is illustrated by showing how DIDS solved an elevator-configuration problem.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46597/1/10845_2004_Article_BF00124685.pd
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