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The paper discusses algorithmic priority inversion in mission-critical machine inference pipelines used in modern neural-network-based perception subsystems and describes a solution to mitigate its effect. In general, priority inversion occurs in computing systems when computations that are "less important" are performed together with or ahead of those that are "more important." Significant priority inversion occurs in existing machine inference pipelines when they do not differentiate between critical and less critical data. We describe a framework to resolve this problem and demonstrate that it improves a perception system's ability to react to critical inputs, while at the same time reducing platform cost. [ABSTRACT FROM AUTHOR]

The ancient oriental game of Go has long been considered a grand challenge for artificial intelligence. For decades, computer Go has defied the classical methods in game tree search that worked so successfully for chess and checkers. However, recent play in computer Go has been transformed by a new paradigm for tree search based on Monte-Carlo methods. Programs based on Monte-Carlo tree search now play at human-master levels and are beginning to challenge top professional players. In this paper, we describe the leading algorithms for Monte-Carlo tree search and explain how they have advanced the state of the art in computer Go. [ABSTRACT FROM AUTHOR]

Recognizing objects in images is an active area of research in computer vision. In the last two decades, there has been much progress and there are already object recognition systems operating in commercial products. However, most of the algorithms for detecting objects perform an exhaustive search across all locations and scales in the image comparing local image regions with an object model. That approach ignores the semantic structure of scenes and tries to solve the recognition problem by brute force. In the real world, objects tend to covary with other objects, providing a rich collection of contextual associations. These contextual associations can be used to reduce the search space by looking only in places in which the object is expected to be; this also increases performance, by rejecting patterns that look like the target but appear in unlikely places. Most modeling attempts so far have defined the context of an object in terms of other previously recognized objects. The drawback of this approach is that inferring the context becomes as difficult as detecting each object. An alternative view of context relies on using the entire scene information holistically. This approach is algorithmically attractive since it dispenses with the need for a prior step of individual object recognition. In this paper, we use a probabilistic framework for encoding the relationships between context and object properties and we show how an integrated system provides improved performance. We view this as a significant step toward general purpose machine vision systems. [ABSTRACT FROM AUTHOR]

Autonomous helicopter flight is widely regarded to be a highly challenging control problem. As helicopters are highly unstable and exhibit complicated dynamical behavior, it is particularly difficult to design controllers that achieve high performance over a broad flight regime. While these aircraft are notoriously difficult to control, there are expert human pilots who are nonetheless capable of demonstrating a wide variety of maneuvers, including aerobatic maneuvers at the edge of the helicopter's performance envelope. In this paper, we present algorithms for modeling and control that leverage these demonstrations to build high-performance control systems for autonomous helicopters. More specifically, we detail our experiences with the Stanford Autonomous Helicopter, which is now capable of extreme aerobatic flight meeting or exceeding the performance of our own expert pilot. [ABSTRACT FROM AUTHOR]

Time and state descriptions form the core of a simulation model representation. The historical influence of initial application areas and the exigencies of language implementations have created a muddled view of the time and state relationships. As a consequence, users of simulation programming languages work in relative isolation; model development, simulation application, model portability, and the communication of results are inhibited and simulation practice fails to contribute to the recognition of an underlying foundation or integrating structure. A model representation structure has been forged from a small set of basic definitions which carefully distinguish the state and time relationships. This paper focuses on the coordination of the time and state concepts using "object" as the link. In addition to clarifying the relationships, the structure relates the concept of ""state-sequenced simulation" to the variations in time flow mechanisms. In conclusion, some speculations are offered regarding alternative algorithms for time flow mechanisms. [ABSTRACT FROM AUTHOR]

This paper suggests a bidirectional relationship between state-space and problem- reduction representations. It resents a formalism based on multiple-input and multiple-output operators which provides a basis for viewing the two types of representations in this manner. A representation of the language recognition problem which is based on the Cocke parsing algorithm Is used as an illustration. A method for representing problems in first-order logic in such a way that the inference system employed by a resolution-based theorem prover determines whether the set of clauses is interpreted in the state-space mode or in the problem-reduction mode is presented. The analogous concepts in problem-reduction and theorem proving, and the terminology used to refer to them, are noted. The relationship between problem-reduction, input resolution, and linear resolution is is discussed. [ABSTRACT FROM AUTHOR]