Since its inception, arti cial intelligence has relied upon a theoretical foundation centred around perfect rationality as the desired property of intelligent systems. We argue, as others have done, that this foundation is inadequate because it imposes fundamentally unsatis able requirements. As a result, there has arisen a wide gap between theory and practice in AI, hindering progress in the eld. We propose instead a property called bounded optimality. Roughly speaking, an agent is bounded-optimal if its program is a solution to the constrained optimization problem presented by its architecture and the task environment. We show how to construct agents with this property for a simple class of machine architectures in a broad class of real-time environments. We illustrate these results using a simple model of an automated mail sorting facility. We also de ne a weaker property, asymptotic bounded optimality (ABO), that generalizes the notion of optimality in classical complexity theory. We then construct universal ABO programs, i.e., programs that are ABO no matter what real-time constraints are applied. Universal ABO programs can be used as building blocks for more complex systems. We conclude with a discussion of the prospects for bounded optimality as a theoretical basis for AI, and relate it to similar trends in philosophy, economics, and game theory.

Code space is a critical issue facing designers of software for embedded systems. Many traditional compiler optimizations are designed to reduce the execution time of compiled code, but not necessarily the size of the compiled code. Further, different results can be achieved by running some optimizations more than once and changing the order in which optimizations are applied. Register allocation only complicates matters, as the interactions between different optimizations can cause more spill code to be generated. The compiler for embedded systems, then, must take care to use the best sequence of optimizations to minimize code space.Since much of the code for embedded systems is compiled once and then burned into ROM, the software designer will often tolerate much longer compile times in the hope of reducing the size of the compiled code. We take advantage of this by using a genetic algorithm to find optimization sequences that generate small object codes. The solutions generated by this algorithm are compared to solutions found using a fixed optimization sequence and solutions found by testing random optimization sequences. Based on the results found by the genetic algorithm, a new fixed sequence is developed to reduce code size. Finally, we explore the idea of using different optimization sequences for different modules and functions of the same program.

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Code space is a critical issue facing designers of software for embedded systems. Many traditional compiler optimizations are designed to reduce the execution time of compiled code, but not necessarily the size of the compiled code. Further, different results can be achieved by running some optimizations more than once and changing the order in which optimizations are applied. Register allocation only complicates matters, as the interactions between different optimizations can cause more spill code to be generated. The compiler for embedded systems, then, must take care to use the best sequence of optimizations to minimize code space.Since much of the code for embedded systems is compiled once and then burned into ROM, the software designer will often tolerate much longer compile times in the hope of reducing the size of the compiled code. We take advantage of this by using a genetic algorithm to find optimization sequences that generate small object codes. The solutions generated by this algorithm are compared to solutions found using a fixed optimization sequence and solutions found by testing random optimization sequences. Based on the results found by the genetic algorithm, a new fixed sequence is developed to reduce code size. Finally, we explore the idea of using different optimization sequences for different modules and functions of the same program.

Who evacuates and why is partially dependent on where one lives because perceptions of risk are not uniformly shared across the area threatened by an approaching hurricane and the same sources and content of information do not have the same effect on evacuation behavior. Hence, efforts to persuade residential populations about risk and when, where, and how to evacuate or shelter in place should originate in the neighborhood rather than emanating from blanket statements from the media or public officials. Our findings also raise important policy questions (included in the discussion section) that require further study and consideration by those responsible with organizing and implementing evacuation plans.