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.
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.
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.
1846400
Common metrics for human-robot interaction
This paper describes an effort to identify common metrics for task-oriented human-robot interaction (HRI). We begin by discussing the need for a toolkit of HRI metrics. We then describe the framework of our work and identify important biasing factors that must be taken into consideration. Finally, we present suggested common metrics for standardization and a case study. Preparation of a larger, more detailed toolkit is in progress.
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.
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.
1465967
Indexing Information for Data Forensics
We introduce novel techniques for organizing the indexing structures of how data is stored so that alterations from an original version can be detected and the changed values specifically identified. We give forensic constructions for several fundamental data structures, including arrays, linked lists, binary search trees, skip lists, and hash tables. Some of our constructions are based on a new reduced-randomness construction for nonadaptive combinatorial group testing.
14885043
Sorting on a parallel pointer machine with applications to set expression evaluation
We present optimal algorithms for sorting on parallel CREW and EREW versions of the pointer machine model. Intuitively, one can view our methods as being based on a parallel mergesort using linked lists rather than arrays (the usual parallel data structure). We also show how to exploit the “locality” of our approach to solve the set expression evaluation problem, a problem with applications to database querying and logic-programming in <italic>O</italic>(log <italic>n</italic>) time using <italic>O</italic>(<italic>n</italic>) processors. Interestingly, this is an asymptotic improvement over what seems possible using previous techniques.
10459418
Constructing Disjoint Paths for Secure Communication
We propose a bandwidth-efficient algorithmic solution for perfectly-secure communication in the absence of secure infrastructure. Our solution involves connecting vertex pairs by a set of k edge-disjoint paths (a structure we call a k-system) where k is a parameter determined by the connectivity of the network. This structure is resilient to adversaries with bounded eavesdropping capability. To ensure that bandwidth is efficiently used we consider connection requests as inputs to the k-Edge Disjoint Path Coloring Problem (k-EDPCOL), a generalization of the Path Coloring Problem, in which each vertex pair is connected by a k-system, and each k-system is assigned a color such that two overlapping k-systems do not have the same color. The objective is to minimize the number of colors. We give a distributed and competitive online algorithm for k-EDPCOL. Additionally, since security applications are our focus we prove that a malicious adversary which attacks the algorithm during the process of construction of a k-system cannot learn anything more than if it had attacked the k-system once it was built.
9843966
Efficient approximation and optimization algorithms for computational metrology
We give efficient algorithms for solving several geometric problems in computational metrology, focusing on the fundamental issues of {open_quotes}flatness{close_quotes} and {open_quotes}roundness.{close_quotes} Specifically, we give approximate and exact algorithms for 2- and 3-dimensional roundness primitives, deriving results that improve previous approaches in several respects, including problem definition, running time, underlying computational model, and dimensionality of the input. We also study methods for determining the width of a d-dimensional point set, which corresponds to the metrology notion of {open_quotes}flatness,{close_quotes} giving an approximation method that can serve as a fast exact-computation filter for this metrology primitive. Finally, we report on experimental results derived from implementation and testing, particularly in 3-space, of our approximation algorithms, including several heuristics designed to significantly speed-up the computations in practice.
207272
Skip-webs: efficient distributed data structures for multi-dimensional data sets
We present a framework for designing efficient distributed data structures for multi-dimensional data. Our structures, which we call skip-webs, extend and improve previous randomized distributed data structures, including skipnets and skip graphs. Our framework applies to a general class of data querying scenarios, which include linear (one-dimensional) data, such as sorted sets, as well as multi-dimensional data, such as d-dimensional octrees and digital tries of character strings defined over a fixed alphabet.We show how to perform a query over such a set of n items spread among n hosts using O(log n/log log n) messages for one-dimensional data, or O(log n) messages for fixed-dimensional data, while using only O(log n) space per host. We also show how to make such structures dynamic so as to allow for insertions and deletions in O(log n) messages for quadtrees, octrees, and digital tries, and O(log n/log log n) messages for one-dimensional data. Finally, we show how to apply a blocking strategy to skip-webs to further improve message complexity for one-dimensional data when hosts can store more data.