Improving design quality and value in the built environment through knowledge of intangibles

Raising design quality and value in the built environment requires continuous improvement, drawing on feedback from clients or occupiers and other industry players. The challenging task for architectural and engineering designers has always been to use their intellectual knowledge to deliver both forms of benefits, tangibles and intangibles, in the built environment. Increasingly as clients demand best value for money, there is a greater need to understand the potential from intangibles, to see projects not as ends in themselves but as means to improved quality of life and wealth creation. As we begin to understand more about how - through the design of the built environment - to deliver these improvements in outcomes, clients will be better placed to expect their successful delivery from designers, and designers themselves will be better placed to provide them. This paper discusses cross-disciplinary issues about intangibles and is aimed at designers, clients, investors and entrepreneurs within the built environment. It presents some findings from a minuscule study that investigated intangible benefits in a new primary school. © 2004 IEEE.

Scaling POMDPs for dialog management with composite summary point-based value iteration (CSPBVI)

Although partially observable Markov decision processes (POMDPs) have shown great promise as a framework for dialog management in spoken dialog systems, important scalability issues remain. This paper tackles the problem of scaling slot-filling POMDP-based dialog managers to many slots with a novel technique called composite point-based value iteration (CSPBVI). CSPBVI creates a "local" POMDP policy for each slot; at runtime, each slot nominates an action and a heuristic chooses which action to take. Experiments in dialog simulation show that CSPBVI successfully scales POMDP-based dialog managers without compromising performance gains over baseline techniques and preserving robustness to errors in user model estimation. Copyright © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.