In its current implementation, the World-Wide Web lacks much of the explicit structure and strong typing found in many closed hypertext systems. While this property has directly fueled the explosive acceptance of the Web, it further complicates the already difficult problem of identifying usable structures and aggregates in large hypertext collections. These reduced structures, or localities, form the basis to simplifying visualizations of and navigation through complex hypertext systems. Much of the previous research into identifying aggregates utilize graph theoretic algorithms based upon structural topology, i.e., the linkages between items. Other research has focused on content analysis to form document collections. This paper presents our exploration into techniques that harness both the topology and textual similarity between items as well as integrate new analyses based upon actual usage of the Xerox's WWW space. Linear equations and spreading activation models are employed to arrange Web pages based upon functional categories, node types, and relevancy. Keywords Information Visualization, World Wide Web, Hypertext.

We introduce a technique for analyzing the behavior of sophisticated A.I. search programs working on realistic, large-scale problems. This approach allows us 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 that captures their principal effects. We compare our model’s predictions with actual data on real problems obtained independently and show that the agreement is quite good. By systematically relaxing our underlying modelling assumptions we identify their relative contribution to the remaining error and then remedy it. We also discuss further applications of our model and suggest how this type of analysis can be generalized to other kinds of A.I. problems. Chapter 1

In this paper we present an approach for global combinatorial optimization applied to the TSP which combines local search heuristics with a population-based strategy. Due to its intrinsic parallelism and the inherent asynchronicity of the method it is specially appealing for MIMD message-passing parallel computers, such as those constructed from transputers. The approach is similar to that used by Muhlenbein [14] [15] [16], Brown et al. [1], Gorges-Schleuter [3] and work performed by the Dynamics of Computation Group at Xerox PARC [4]. We consider them as prototype examples of "memetic" algorithms in the sense described in Ref. [12] (see also Ref. [5]). A preliminary description of our work can also be found in Ref. [17]. 1 Introduction The approach is based on a population of a number of distinguishable individuals called agents (following Huberman's glossary [4] [5]), which are involved in periods of independent search interspersed with periods in which they interact. Individuals...

Many problem ensembles exhibit a phase transition that is associated with a large peak in the average cost of solving the problem instances. However, this peak is not necessarily due to a lack of solutions: indeed the average number of solutions is typically exponentially large. Here, we study this situation within the context of the satisfiability transition in Random 3SAT. We find that a significant subclass of instances emerges as we cross the phase transition. These instances are characterized by having about 85--95% of their variables occurring in unary prime implicates (UPIs), with their remaining variables being subject to few constraints. In such instances the models are not randomly distributed but all lie in a cluster that is exponentially large, but still admits a simple description. Studying the effect of UPIs on the local search algorithm Wsat shows that these "single-cluster" instances are harder to solve, and we relate their appearance at the phase transition to the peak...

When search techniques are used to solve a practical problem, the solution produced is often brittle in the sense that small execution difficulties can have an arbitrarily large effect on the viability of the solution. The AI community has responded to this difficulty by investigating the development of "robust problem solvers" that are intended to be proof against this difficulty. We argue that robustness is best cast not as a property of the problem solver, but as a property of the solution. We introduce a new class of models for a logical theory, called supermodels, that captures this idea. Supermodels guarantee that the model in question is robust, and allow us to quantify the degree to which it is so. We investigate the theoretical properties of supermodels, showing that finding supermodels is typically of the same theoretical complexity as finding models. We provide a general way to modify a logical theory so that a model of the modified theory is a supermodel of the original. Ex...

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We present a quantitative assessment of the value of cooperation for solving constraint satisfaction problems through a series of experiments, as well as a general theory of cooperative problem solving. These experiments, using both hierarchical and non-hierarchical cooperation, clearly exhibit a universal improvement in performance that results from cooperation. We also show both theoretically and experimentally the super-linear speed-up that results from having a diverse collection of skills among the cooperating agents. Our results suggest an alternative methodology to existing techniques for solving constraint satisfaction problems in computer science and distributed artificial intelligence.

You are chief of protocol for the embassy ball. The crown prince instructs you either to invite Peru or to exclude Qatar. The queen asks you to invite either Qatar or Romania or both. The king, in a spiteful mood, wants to snub either Romania or Peru or both. Is there a guest list that will satisfy the whims of the entire royal family? This contrived little puzzle is an instance of a problem that lies near the root of theoretical computer science. It is called the satisfiability problem, or SAT, and it was the first member of the notorious class known as NP-complete problems. These are computational tasks that seem intrinsically hard, but after 25 years of effort no one has yet proved

The statistical mechanics of combinatorial search problems is described using the example of the well-known NP-complete graph coloring problem. We focus on a recently identified phase transition from under- to overconstrained problems, near which are concentrated many hard to solve search problems. Thus, a readily computed measure of problem structure predicts the difficulty of solving the problem, on average. However, this prediction is associated with a large variance and depends on the somewhat arbitrary choice of the problem ensemble. Thus these results are of limited direct use for individual instances. To help address this limitation, additional parameters, describing problem structure as well as heuristic effectiveness, are introduced. This also highlights the distinction between the statistical mechanics of combinatorial search problems, with their exponentially large search spaces, and physical systems, whose interactions are often governed by a simple euclidean metric. Chapter 1

We describe the development of a computational cognitive model that explains navigation behavior on the World Wide Web. The model, called SNIF-ACT (Scent-based Navigation and Information Foraging in the ACT cognitive architecture), is motivated by Information Foraging Theory (IFT), which quantifies the perceived relevance of a Web link to a user’s goal by a spreading activation mechanism. The model assumes that users evaluate links on a Web page sequentially and decide to click on a link or to go back to the previous page by a Bayesian satisficing model (BSM) that adaptively evaluates and selects actions based on a combination of previous and current assessments of the relevance of link texts to information goals. SNIF-ACT 1.0 utilizes the measure of utility, called information Wai-Tat Fu is an applied cognitive scientist with interests in human–computer interaction, cognitive modeling, information seeking, interactive decision making, and cognitive skill acquisition; he is an Assistant Professor in the Human Factors Division and Beckman Institute of Advanced Science and Technology at the University