Venue: Massachusetts Institute of Technology
Flexible Computation in Intelligent Systems: Results, Issues, and Opportunities
Flexible computation refers to procedures that allow a graceful trade-off to be made between the quality of results and allocations of costly resources, such as time, memory, or information. Systems employing flexible computation gain the ability to adapt the quality of their response to dynamic changes in requirements for precision, and to uncertainty or variation in the cost of computational commodities. Recent examples of flexible computation techniques include memory-bounded search, anytime algorithms, approximate query processing, and a variety of imprecise computation techniques. Flexible computation has been applied to combinatorial optimization, planning, probabilistic inference, decision making, and theorem proving. Our goal was to explore results, critical problems, and opportunities via invited talks, presentation papers, and panel discussions.
Fall and Spring Symposia are sponsored by the American Association for Artificial Intelligence (AAAI (opens in new tab)).