Future generations of air traffic management systems may give appropriately equipped aircraft the freedom to change flight paths in real time. This would require a conflict avoidance and resolution scheme that is both decentralized and cooperative. We describe a multiagent solution to aircraft conflict resolution based on satisficing game theory. A key feature of the theory is that satisficing decision makers form their preferences by taking into consideration the preferences of others, unlike conventional game theory that models agents that maximize self-interest metrics. This makes possible situational altruism, a sophisticated form of unselfish behavior in which the preferences of another agent are accommodated provided that the other agent will actually take advantage of the sacrifice. This approach also makes possible the creation of groups in which every decision maker receives due consideration. We present simulation results from a variety of scenarios in which the aircraft are limited to constant-speed heading-change maneuvers to avoid conflicts. We show that the satisficing approach results in behavior that is attractive both in terms of safety and performance. The results underscore the applicability of satisficing game theory to multiagent problems in which self-interested participants are inclined to cooperation.

Searching a random graph is known to be computationally complex. The classic example for the single agent case is the traveling salesman problem. In this paper, we generalize to the multi-agent case where n autonomous agents must cooperate to visit all the nodes of a random graph in an efficient manner. We impose a limitation on the agent's ability to communicate with each other. A satisficing game approach is ideally suited to this problem since it accommodates multiple solutions for each player and provides the opportunity for negotiation. We demonstrate performance of this approach on both deterministic and random graphs with up to 100 nodes.

Social utilities account for agent preferences and, thus, can characterize complex interrelationships, such as cooperation, compromise, negotiation, and altruism, that can exist between agents. Satisficing game theory, which is based on social utilities, offers a framework within which to design sophisticated multiagent systems. Key features of this approach are: a) an N-agent system may be represented by a 2N-dimensional Bayesian network, called a praxeic network; b) the theory accommodates a notion of situational altruism (a willingness to defer to others in a controlled way if so doing would actually benefit others under the condition that others wish to take advantage of such largesse); and c) satisficing games admits a protocol for effective negotiation between agents who, though interested in their own welfare, are also willing to give some deference to others. Three applications are presented. The first two involve well-known two-person games: the Prisoner's Dilemma and the Battle of the Sexes, and the third is a simulated uninhabited aerial vehicle scenario.

Addresses the problem of positioning a group of autonomous but coordinating mobile robots into a specified spatial configuration, with each position occupied by a single robot. Specifically, we assume that there is no central controller or inter-agent communication, and require that the robots must move into position without collision or unnecessary delay. We present an approach to this problem based on praxeic utility theory and evaluate its effectiveness, with particular emphasis on computational feasibility.

Model-predictive control, which is an alternative to conventional optimal control, provides controller solutions to many constrained and nonlinear control problems. However, even when a good model is available, it may be necessary for an expert to specify the relationship between local model predictions and global system performance. We present a satisficing fuzzy logic controller that is based on a receding control horizon, but which employs a fuzzy description of system consequences via model predictions. This controller considers the gains and losses associated with each control action, is compatible with robust design objectives, and permits flexible defuzzifier design. We demonstrate the controller's application to representative problems from the control of uncertain nonlinear systems.

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.

A reasonable measure of quantizer complexity is the expected number of quanta per input sample for which distortion is computed. Given this measure, a rate-distortion-complexity theory is obtained by extending earlier work in alphabet-constrained rate-distortion theory. Numerical results show that operation on the alphabet-constrained rate-distortion bound can be obtained with a complexity of two. Furthermore, Lloyd-Max conditions are shown to describe the minimum of a slightly constrained version of the rate-distortion-complexity problem. Complexity-constrained design methods are applied first to trellis-coded quantizers, where they are shown to reduce arithmetic operations by at least 25%. They are then used to develop model-based trellis quantizers, the trellises of which are derived from a Markov model of the source. Simulation results confirm that excellent performance can be obtained with modest complexity.

This brief contribution introduces a method of quantifying the locality in a given reference sequence and visually representing it as a three dimensional surface. We explore some of the properties of this formulation of locality and show the correlation between graphical features and specific reference patterns. The utility of our formulation of locality is demonstrated through two of its potential applications as a visualizations tool: characterizing and summarizing workload locality, and evaluating the effectiveness of benchmark programs in exercising memory hierarchies.

Error spectrum shaping (ESS) quantizers are analyzed in the context of realistic FIR reconstruction filters and nonzero probability of saturation. Conditions for stable operation are derived, and a suboptimal but effective and computationally efficient design method is proposed. Excellent agreement is obtained between the analysis and simulations. Even under these more stringent constraints, ESS quantizers are shown to achieve significant signal-to-quantization-noise ratio (SQNR) gains when quantizing oversampled Gaussian sources.

A method is developed for decreasing the computational complexity of a trellis quantizer (TQ) encoder. We begin by developing a rate-distortion theory under a constraint on the average instantaneous number of quanta considered. This constraint has practical importance: in a TQ, the average instantaneous number of quanta is exactly the average number of multiplies required at the encoder. The theory shows that if the conditional probability of each quanta is restricted to a finite region of support, the instantaneous number of quanta considered can be made quite small at little or no cost in SQNR performance. Simulations of TQs confirm this prediction. This reduction in complexity makes practical the use of model-based TQs (MTQs), which had previously been considered computationally unreasonable. For speech, performance gains of several dB SQNR over adaptive predictive schemes at a similar computational complexity are obtained using only a first-order MTQ.

The use of direct sum codebooks to minimize the memory requirements of vector quantizers is investigated. Assuming arbitrary fixed partitions, necessary conditions for minimum distortion codebooks are derived, first for scalar codebooks, assuming mean-squared error distortion, and then for vector codebooks and a broader class of distortion measures. An iterative procedure is described for designing locally optimal direct sum codebooks. Both optimal and computationally efficient suboptimal encoding schemes are considered. It is shown that although an optimal encoding can be implemented by a sequential encoder, the complexity of implementing optimal stagewise partitions generally exceeds the complexity of an exhaustive search of the direct sum codebook. It is also shown that sequential nearest-neighbor encoders can be extremely inefficient. The M-search method is explored as one method of improving the effectiveness of suboptimal sequential encoders. Representative results for simulated direct sum quantizers are presented.>

This paper shows that tree search encoders are ineffective when used to determine the closest code vector in residual quantizer (RQ) alphabets. In particular, the equivalent cell boundaries are poorly chosen and the labelling of equivalent code vectors produced by the decoder and by a tree-structured encoder are inconsistent. This problem does not arise when two-level RQ alphabets are used in trellis coded vector quantizers. Trellis-coded RQs are designed for the memoryless Gaussian, Laplacian, and Gauss-Markov sources at a rate of R=1 bit per sample with encouraging results; a SQNR of 5.92 dB has been achieved on the Gaussian source.>

It is pointed out that previous analyses of ESS (error spectrum shaping) quantizers did not consider either the limitations of realizable reconstruction filters or the effects of quantizer saturation. Consequently, predictions of ESS distortion were often optimistic. The authors extend the original analysis to include the use of nonideal reconstruction filters. Simulations show that this extended analysis predicts accurately the distortion of nonsaturating ESS quantizers. An attempt to account analytically for saturation effects led to a difficult nonlinear design problem. A constraint on the energy feedback filter impulse response was effective in suppressing saturation and led to a straightforward linear optimization problem.>

The authors present an algorithm for the generation of codebooks from a set of training vectors using simulated annealing. Convergence of the algorithm to the globally optimal codebook in finite time is proved, and experimental results comparing simulated annealing with Lloyd algorithms for image quantization are presented. The experimental results indicate that the proposed algorithm obtains the best known codebook for the experimental situation described by R.M. Gray and E.D. Karnin (IEEE Trans. on Inf. Theory, vol.IT-28, no.2, p.256-61, Mar. 1982). It has also been demonstrated that this technique works well for the construction of codebooks from real image data.>

We consider the design of imaging systems for which image restoration is a well-conditioned problem. By assuming that both the observation and the object are discrete, we are able to derive, via the Singular Value Decomposition, conditions which minimize the condition number for either coherent or incoherent illumination systems. We also illustrate the quality of signals restored by these well-conditioned systems.

We study in this paper a discrete-time, discrete-frequency model for image restoration using the singular value decomposition of the imaging matrix. We show that the resulting singular vectors have many of the properties possessed by Slepian's discrete prolate sphroidal sequences (DPSS). They are doubly orthogonal, they are bandlimited, they satisfy an equation very similar to that satisfied by the DPSSs, and they possess an extremal energy-concentration property. These properties continue to hold with appropriate modification for bandpass as well as lowpass operations.

A problem receiving considerable attention from researchers is that of producing a stable extrapolation of the Fourier transform of a function of finite support. The problem is ill-posed, and the sensitivity of the extrapolated result to measurement noise is pronounced for many of the solution algorithms proposed to date, In this paper an alternative algorithm is presented. This new algorithm is non-linear, and in certain cases can produce very accurate extrapolations over a wide range of signal-to-noise ratios. We discuss in rather heuristic terms the behavior of this algorithm, and summarize the results of some experiments.

This paper describes a fast direct algorithm for obtaining least-squares phase estimates from arrays of noisy phase differences. The algorithm uses the fast Fourier transform (FFT) to diagonalize and decouple the system of equations which results from the application of the least-squares criterion. It is accurate and stable, and is perhaps an order of magnitude faster than the best iterative method. The effectiveness of the algorithm has been demonstrated by using it in connection with the Knox-Thompson speckle-imaging procedure to restore an optical object perturbed by simulated atmospheric turbulence.