### Topics

### features

### Publications

### Issue Archive

# Algorithms for Learning Preferences for Sets of Objects

### The user gives examples of preferred sets; the algorithms do the rest.

A method is being developed that provides for an artificial-intelligence system to learn a user’s preferences for sets of objects and to thereafter automatically select subsets of objects according to those preferences. The method was originally intended to enable automated selection, from among large sets of images acquired by instruments aboard spacecraft, of image subsets considered to be scientifically valuable enough to justify use of limited communication resources for transmission to Earth. The method is also applicable to other sets of objects: examples of sets of objects considered in the development of the method include food menus, radio-station music playlists, and assortments of colored blocks for creating mosaics.

# Algorithms for High-Speed Noninvasive Eye-Tracking System

### One of the algorithms enables tracking at a frame rate of several kilohertz.

Two image-data-processing algorithms are essential to the successful operation of a system of electronic hardware and software that noninvasively tracks the direction of a person’s gaze in real time. The system was described in “High-Speed Noninvasive Eye-Tracking System” (NPO-30700) *NASA Tech Briefs*, Vol. 31, No. 8 (August 2007), page 51.

# Model for Simulating a Spiral Software-Development Process

### A prior model for simulating a waterfall process has been extended.

A discrete-event simulation model, and a computer program that implements the model, have been developed as means of analyzing a spiral software-development process. This model can be tailored to specific development environments for use by software project managers in making quantitative cases for deciding among different software-development processes, courses of action, and cost estimates.

# Adapting ASPEN for Orbital Express

### Declarative modeling brings efficiency to encoded procedures and allows for guarantees on resource usage and time usage.

By studying the Orbital Express mission, modeling the spacecraft and scenarios, and testing the system, a technique has been developed that uses recursive decomposition to represent procedural actions declaratively, schemalevel uncertainty reasoning to make uncertainty reasoning tractable, and lightweight, natural language processing to automatically parse procedures to produce declarative models.

# Algorithm That Synthesizes Other Algorithms for Hashing

### A synthesized algorithm is guaranteed to be executable in constant time.

An algorithm that includes a collection of several subalgorithms has been devised as a means of synthesizing still other algorithms (which could include computer code) that utilize hashing to determine whether an element (typically, a number or other datum) is a member of a set (typically, a list of numbers). Each subalgorithm synthesizes an algorithm (e.g., a block of code) that maps a static set of key hashes to a somewhat linear monotonically increasing sequence of integers. The goal in formulating this mapping is to cause the length of the sequence thus generated to be as close as practicable to the original length of the set and thus to minimize gaps between the elements.

# Physics of Life: A Model for Non-Newtonian Properties of Living Systems

### New analytical tools focus on the geometry and kinematics of behavior of living things.

This innovation proposes the reconciliation of the evolution of life with the second law of thermodynamics via the introduction of the First Principle for modeling behavior of living systems. The structure of the model is quantum-inspired: it acquires the topology of the Madelung equation in which the quantum potential is replaced with the information potential. As a result, the model captures the most fundamental property of life: the progressive evolution; i.e. the ability to evolve from disorder to order without any external interference.

# Parameterized Linear Longitudinal Airship Model

A parameterized linear mathematical model of the longitudinal dynamics of an airship is undergoing development. This model is intended to be used in designing control systems for future airships that would operate in the atmospheres of Earth and remote planets.

# Reactive Collision Avoidance Algorithm

### Algorithm is used for safe operation of autonomous, collaborative, vehicle formations.

The reactive collision avoidance (RCA) algorithm allows a spacecraft to find a fuel-optimal trajectory for avoiding an arbitrary number of colliding spacecraft in real time while accounting for acceleration limits. In addition to spacecraft, the technology can be used for vehicles that can accelerate in any direction, such as helicopters and submersibles.

# Modeling Common-Sense Decisions in Artificial Intelligence

### Common sense is implemented partly by feedback from mental to motor dynamics.

A methodology has been conceived for efficient synthesis of dynamical models that simulate common-sense decision-making processes. This methodology is intended to contribute to the design of artificial-intelligence systems that could imitate human commonsense decision making or assist humans in making correct decisions in unanticipated circumstances. This methodology is a product of continuing research on mathematical models of the behaviors of single- and multi-agent systems known in biology, economics, and sociology, ranging from a single-cell organism at one extreme to the whole of human society at the other extreme. Earlier results of this research were reported in several prior *NASA Tech Briefs* articles, the three most recent and relevant being “Characteristics of Dynamics of Intelligent Systems” (NPO-21037), *NASA Tech Briefs*, Vol. 26, No. 12 (December 2002), page 48; “Self-Supervised Dynamical Systems” (NPO-30634), *NASA Tech Briefs*, Vol. 27, No. 3 (March 2003), page 72; and “Complexity for Survival of Living Systems” (NPO-43302), *NASA Tech Briefs*, Vol. 33, No. 7 (July 2009), page 62.

# Fast Solution in Sparse LDA for Binary Classification

### Special properties of binary classification and greedy algorithms enable speedup.

An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable-selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bioinformatics. Because of its combinatorial nature, feature- or variable-selection problems are “NP-hard” or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms.