29722627
A theory of satisficing decisions and control
The existence of an optimal control policy and the techniques for finding it are grounded fundamentally in a global perspective. These techniques can be of limited value when the global behaviour of the system is difficult to characterize, as it may be when the system is nonlinear, when the input is constrained, or when only partial information is available regarding system dynamics or the environment. Satisficing control theory is an alternative approach that is compatible with the limited rationality associated with such systems. This theory is extended by the introduction of the notion of strong satisficing to provide a systematic procedure for the design of satisficing controls. The power of the satisficing approach is illustrated by applications to representative control problems.
17845810
A binary-categorization approach for classifying multiple-record Web documents using application ontologies and a probabilistic model
The amount of information available on the World Wide Web has been increasing dramatically in recent years. To enhance speedy searching and retrieving Web documents of interest, researchers and practitioners have partially relied on various information retrieval techniques. We propose a probabilistic model to classify Web documents into relevant documents and irrelevant documents with respect to a particular application ontology, which is a conceptual-model snippet of standard ontologies. Our probabilistic model is based on multivariate statistical analysis and is different from the conventional probabilistic information retrieval models. The experiments we have conducted on a set of representative Web documents indicate that the proposed probabilistic model is promising in binary-categorization of multiple-record Web documents.
13562077
Satisficing Equilibria: A Non-Classical Theory of Games and Decisions
Satisficing, or being “good enough,” is the fundamental obligation of rational decision makers. We cannot rationally choose an option, even when we do not know of anything better, unless we know it is good enough. Unfortunately, we are not often in the position of knowing that there could be no better option, and hence that the option must be good enough. A complete search through all logical possibilities is often impractical, particularly in multi-agent contexts, due to excessive computational difficulty, modeling complexity, and uncertainty. It can be equally impractical, if it is even possible, to determine the cost of the additional required search to find an option that is good enough. In a departure from the traditional notion of satisficing as a species of bounded rationality, satisficing is here redefined in terms of a notion of intrinsic rationality. Epistemic utility theory serves as the philosophical foundation of a new praxeological decision-making paradigm of satisficing equilibria that is applicable to both single- and multiple-agent scenarios. All interagent relationships are modeled by an interdependence function that explicitly accommodates both self and group interest, from which multilateral and unilateral selectability and rejectability mass functions can be derived and compared via the praxeic likelihood ratio test.
43673902
Coordinated target assignment and intercept for unmanned air vehicles
Presents an end-to-end solution to the battlefield scenario where M unmanned air vehicles are assigned to strike N known targets, in the presence of dynamic threats. The problem is decomposed into the subproblems of (1) cooperative target assignment, (2) coordinated UAV intercept, (3) path planning, and (4) feasible trajectory generation. The design technique is based on a hierarchical approach to coordinated control. Detailed simulation results are presented.
6463761
Snapshots for semantic maps
A significant area of research in mobile robotics is in the local representation of a remote environment. In order to include a human in a mobile robot task it becomes important to present the remote information efficiently to a human. A relatively new approach to information presentation is semantic maps. Semantic maps provide more detail about an environment than typical maps because they are augmented by icons or symbols that provide meaning for places or objects of interest. In this paper we present snapshot technology as a means to take pictures from the real world and store them in a semantic map. To make the snapshots and semantic map available to an operator, we identify and discuss general attributes for useful displays and present a mixed reality 3D interface that meets the requirements. The interface and snapshot technology are validated through experiments in real and simulated environments.
12614052
Learning haptic feedback for guiding driver behavior
Information about the driving state can be conveyed to automobile drivers through force feedback signals sent via the pedals and steering wheel. Because the set of possible haptic signals and driver responses is huge, it is desirable to automatically learn which signals are most useful to drivers. Thus, it is instructive to explore how machine learning techniques can be used as a step in the design of a haptic interface system. In this paper, we present a learning algorithm that learns useful haptic feedback and apply the algorithm to learning feedback for automobile drivers. We present evidence to show that the algorithm is sensitive enough to learn useful feedback under some circumstances, but that its scope may be limited by people's ability to act as admittance controllers.
13651233
Planar Separators and Parallel Polygon Triangulation
Abstract We show how to construct an O (√ n )-separator decomposition of a planar graph G in O ( n ) time. Such a decomposition defines a binary tree, where each node corresponds to a subgraph of G and stores an O (√ n )-separator of that subgraph. We also show how to construct an O ( n ϵ )-way decomposition tree in parallel in O (log n ) time so that each node corresponds to a subgraph of G and stores an O ( n 12+ϵ )-separator of that subgraph. We demonstrate the utility of such a separator decomposition by showing how it can be used in the design of a parallel algorithm for triangulating a simple polygon deterministically in O (log n ) time using O ( n /log n ) processors on a CRCW PRAM.
542516
Balanced Aspect Ratio Trees Revisited
Spatial databases support a variety of geometric queries on point data such as range searches, nearest neighbor searches, etc. Balanced Aspect Ratio (BAR) trees are hierarchical space decomposition structures that are general-purpose and space-efficient, and, in addition, enjoy a worst case performance poly-logarithmic in the number of points for approximate queries. They maintain limits on their depth, as well as on the aspect ratio (intuitively, how skinny the regions can be). BAR trees were initially developed for 2 dimensional spaces and a fixed set of partitioning planes, and then extended to d dimensional spaces and more general partitioning planes. Here we revisit 2 dimensional spaces and show that, for any given set of 3 partitioning planes, it is not only possible to construct such trees, it is also possible to derive a simple closed-form upper bound on the aspect ratio. This bound, and the resulting algorithm, are much simpler than what is known for general BAR trees. We call the resulting BAR trees Parameterized BAR trees and empirically evaluate them for different partitioning planes. Our experiments show that our theoretical bound converges to the empirically obtained values in the lower ranges, and also make a case for using evenly oriented partitioning planes.
2560451
Teaching data structure design patterns
In this paper we present an approach for teaching the Freshman-Sophomore introduction to data structures course (CS2) in a way that provides an introduction to object-oriented software engineering patterns in addition to the theory of data structures. We survey in this paper several design patterns and describe how they can be naturally integrated in the CS2 curriculum.
7630937
Using randomization in the teaching of data structures and algorithms
We describe an approach for incorporating randomization in the teaching of data structures and algorithms. The proofs we include are quite simple and can easily be made a part of a Freshman-Sophomore Introduction to Data Structures (CS2) course and a Junior-Senior level course on the design and analysis of data structures and algorithms (CS7/DS&amp;A). The main idea of this approach is to show that using randomization in data structures and algorithms is safe and can be used to significantly simplify efficient solutions to various computational problems. We illustrate this approach by giving examples of the use of randomization in some traditional topics from CS2 and DS&amp;A.
1270229
Fast Randomized Parallel Methods for Planar Convex Hull Construction
Abstract We present a number of efficient parallel algorithms for constructing 2-dimensional convex hulls on a randomized CRCW PRAM. Specifically, we show how to build the convex hull of n presorted points in the plane in O(1) time using O(n log n) work, with n-exponential probability, or, alternately, in O ( log ∗ n) time using O(n) work, with n-exponential probability. We also show how to find the convex hull of n unsorted planar points in O(log n) time using O(n log h) work, with n-exponential probability, where h is the number of edges in the convex hull (h is O(n), but can be as small as O(1)). Our algorithm for unsorted inputs depends on the use of new in-place procedures, that is, procedures that are defined on a subset of elements in the input and that work without reordering the input. In order to achieve our n-exponential confidence bounds we use a new parallel technique called failure sweeping.