Eye movements read by a new AI application can reveal thoughts, memories, goals -- and brain diseases. A new tool developed at the Kavli Institute for Systems Neuroscience in Norway and described in an article in Nature Neuroscience, predicts gaze direction and eye movement directly from magnetic resonance imaging (MRI) scans. The goal is to make eye tracking diagnostics a standard in brain imaging research and hospital clinics. Whenever you explore an environment or search for something, you scan the scene using continuous rapid eye movements. Your eyes also make short stops to fixate on certain elements of the scene that you want more detailed information about.

The quest for new and better actuation technologies and'soft' robotics is often based on principles of biomimetics, in which machine components are designed to mimic the movement of human muscles -- and ideally, to outperform them. Despite the performance of actuators like electric motors and hydraulic pistons, their rigid form limits how they can be deployed. As robots transition to more biological forms and as people ask for more biomimetic prostheses, actuators need to evolve. Associate professor (and alum) Michael Shafer and professor Heidi Feigenbaum of Northern Arizona University's Department of Mechanical Engineering, along with graduate student researcher Diego Higueras-Ruiz, published a paper in Science Robotics presenting a new, high-performance artificial muscle technology they developed in NAU's Dynamic Active Systems Laboratory. The paper, titled "Cavatappi artificial muscles from drawing, twisting, and coiling polymer tubes," details how the new technology enables more human-like motion due to its flexibility and adaptability, but outperforms human skeletal muscle in several metrics.

Conventional Neural Networks can approximate simple arithmetic operations, but fail to generalize beyond the range of numbers that were seen during training. Neural Arithmetic Units aim to overcome this difficulty, but current arithmetic units are either limited to operate on positive numbers or can only represent a subset of arithmetic operations. We introduce the Neural Power Unit (NPU) that operates on the full domain of real numbers and is capable of learning arbitrary power functions in a single layer. The NPU thus fixes the shortcomings of existing arithmetic units and extends their expressivity. We achieve this by using complex arithmetic without requiring a conversion of the network to complex numbers. A simplification of the unit to the RealNPU yields a highly transparent model. We show that the NPUs outperform their competitors in terms of accuracy and sparsity on artificial arithmetic datasets, and that the RealNPU can discover the governing equations of a dynamical system only from data.

J. H. Conway, On Numbers and Games, Academic Press, New In this article, a number of concepts that are of importance York, 1976. in research on game-playing programs have been J. H. Conway, "All Games Bright and Beautiful," Am.

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.

The SHOP2 planning system received one of the awards for distinguished performance in the 2002 International Planning Competition. This paper describes the features of SHOP2 which enabled it to excel in the competition, especially those aspects of SHOP2 that deal with temporal and metric planning domains.

For more details, see Nau et al. 's preconditions can include logical inferences, 's preconditions two methods for traveling from one location can include Horn-clause inferencing, numeric to another: (1) traveling by airplane and (2) computations, and calls to external programs. 's expressive power can be used to create a totally ordered list of subtasks. Suppose domain representations for complex application that all these subtasks are primitive except for domains. For example, the Horn 4. if t is primitive (i.e., there is an operator for t) then clauses can include calls to attached procedures 5. nondeterministically choose an operator o for t We believe the primary 14. endif's higher level of expressivity made it possible to formulate highly expressive domain algorithms in's data structures to make them faster; for example, we found that a simple change to the data structure We intend to make more optimizations in the near future. (Aha and Breslow 1997).

A computer program that uses AI planning techniques is now the world champion computer program in the game of Contract Bridge. As reported in The New York Times and The Washington Post, this program -- a new version of Great Game Products' BRIDGE BARON program -- won the Baron Barclay World Bridge Computer Challenge, an international competition hosted in July 1997 by the American Contract Bridge League. It is well known that the game tree search techniques used in computer programs for games such as Chess and Checkers work differently from how humans think about such games. In contrast, our new version of the BRIDGE BARON emulates the way in which a human might plan declarer play in Bridge by using an adaptation of hierarchical task network planning. This article gives an overview of the planning techniques that we have incorporated into the BRIDGE BARON and discusses what the program's victory signifies for research on AI planning and game playing.

Empirical evidence suggests that searching deeper in game trees using the minimax propagation rule usually improves the quality of decisions significantly. However, despite many recent theoretical analyses of the effects of minimax look ahead, however, this phenomenon has still not been convincingly explained. Instead, much attention has been given to so-called pathological behavior, which occurs under certain assumptions. This article supports the view that pathology is a direct result of these underlying theoretical assumptions. Pathology does not occur in practice, because these assumptions do not apply in realistic domains. The article presents several arguments in favor of minimaxing and focuses attention on the gap between their analytical formulation and their practical meaning. A new model is presented based on the strict separation of static and dynamic aspects in practical programs. finally, certain methods of improving minimax look-ahead are discussed, drawing on insights gained from this research.