As you probably already know, there is an active discussion going o n i n forums ranging from lunch-table conversations to workshops on "strategic directions" to formal r epo r t s l r ega rd ing the future of theoret ical computer science. Since your complexity columnist does not know The Answer, I 've asked a number of people to contr ibute their comments on the narrower issue of the future o...

This paper describes a cognitively-oriented architecture that facilitates the development of expertise. Based on knowledge about human decision making, it integrates multiple representations, multiple decision-making rationales, and multiple learning methods to support the construction of intelligent systems. A constraint solver implemented within the architecture engineers a problem-solving pa...

Two subproblems that arise when routing channels with interchangeable terminals are shown to be NP-hard. These problems are: P1: Is there a net to terminal assignment that results in an acyclic vertical constraint graph? P2: For instances with acyclic vertical constraint graphs, obtain net to terminal assignments for which the length of the longest path in the vertical constraint graph is minimum.

The field of exact exponential time algorithms for NP-hard problems has thrived over the last decade. While exhaustive search remains asymptotically the fastest known algorithm for some basic problems, difficult and non-trivial exponential time algorithms have been found for a myriad of problems, including GRAPH COLORING, HAMILTONIAN PATH, DOMINATING SET and 3-CNF-SAT. In some instances, improv...

This paper studies a version of the job shop scheduling problem in which some operations have to be scheduled within non-relaxable time windows (i.e. earliesv'latest possible stan time windows). This problem is a well-known NP-complete Constraint Satisfaction Roblem (CSP). A popular method for solving this type of problems consists in using depth-first backtrack scarch. Our earlier work focused...

Is cognitive science relevant to AI problems? Yes — but only when these problems are sufficiently hard. When they qualify as such, the best move for the clever AI researcher is to turn not to yet another faster machine bestowed by Moore’s Law, and not to some souped-up version of an instrument in the AI toolbox, but rather to the human mind’s approach to the problem in question. Despite Turing’...

Many types of problem exhibit a phase transition as a problem parameter is varied, from a region where most problems are easy and soluble to a region where most problems are easy but insoluble. In the intervening phase transition region, the median problem di culty is greatest. However, occasional exceptionally hard problems (ehps) can be found in the easy and soluble region: these problems can...

One usually writes A.I. programs to be used on a range of examples which, although similar in kind, differ in detail. This paper shows how to predict where, in a space of problem instances, the hardest problems are to be found and where the fluctuations in difficulty are greatest. Our key insight is to shift emphasis from modelling sophisticated algorithms directly to modelling a search space w...

We study optimization problems that are neither approximable in polynomial time (at least with a constant factor) nor fixed parameter tractable, under widely believed complexity assumptions. Specifically, we focus on MAXIMUM INDEPENDENT SET, VERTEX COLORING, SET COVER, and BANDWIDTH. In recent years, many researchers design exact exponential-time algorithms for these and other hard problems. Th...

The discrete gate sizing problem has been studied by several researchers recently. Some Complexity results have been obtained, ana' a number of heuristic algorithms have been proposed. For circuit networks that are restricted to the set of trees, or series-parallel graphs pseudo-polynomial time algorithms to obtain the exact solution have also been proposed, though none can be extended to arbit...