Browsing by Author "Huang, Di-Wei"
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Item Exploring the Computational Explanatory Gap(MDPI, 2017-01-16) Reggia, James A.; Huang, Di-Wei; Katz, GarrettWhile substantial progress has been made in the field known as artificial consciousness, at the present time there is no generally accepted phenomenally conscious machine, nor even a clear route to how one might be produced should we decide to try. Here, we take the position that, from our computer science perspective, a major reason for this is a computational explanatory gap: our inability to understand/explain the implementation of high-level cognitive algorithms in terms of neurocomputational processing. We explain how addressing the computational explanatory gap can identify computational correlates of consciousness. We suggest that bridging this gap is not only critical to further progress in the area of machine consciousness, but would also inform the search for neurobiological correlates of consciousness and would, with high probability, contribute to demystifying the “hard problem” of understanding the mind–brain relationship. We compile a listing of previously proposed computational correlates of consciousness and, based on the results of recent computational modeling, suggest that the gating mechanisms associated with top-down cognitive control of working memory should be added to this list. We conclude that developing neurocognitive architectures that contribute to bridging the computational explanatory gap provides a credible and achievable roadmap to understanding the ultimate prospects for a conscious machine, and to a better understanding of the mind–brain problem in general.Item The Maryland Virtual Demonstrator Environment for Robot Imitation Learning(2014-06-20) Huang, Di-Wei; Katz, Garrett E.; Gentili, Rodolphe J.; Reggia, James A.Robot imitation learning, where a robot autonomously generates actions required to accomplish a task demonstrated by a human, has emerged as a potential replacement for a more conventional hand-coded approach to programming robots. Many past studies in imitation learning have human demonstrators perform tasks in the real world. However, this approach is generally expensive and requires high-quality image processing and complex human motion understanding. To address this issue, we developed a simulated environment for imitation learning, where visual properties of objects are simplified to lower the barriers of image processing. The user is provided with a graphical user interface (GUI) to demonstrate tasks by manipulating objects in the environment, from which a simulated robot in the same environment can learn. We hypothesize that in many situations, imitation learning can be significantly simplified while being more effective when based solely on objects being manipulated rather than the demonstrator's body and motions. For this reason, the demonstrator in the environment is not embodied, and a demonstration as seen by the robot consists of sequences of object movements. A programming interface in Matlab is provided for researchers and developers to write code that controls the robot's behaviors. An XML interface is also provided to generate objects that form task-specific scenarios. This report describes the features and usages of the software.Item Self-Organizing Map Neural Architectures Based on Limit Cycle Attractors(2016) Huang, Di-Wei; Reggia, James A.; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Recent efforts to develop large-scale neural architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason is that most conventional SOMs use a static encoding representation: Each input is typically represented by the fixed activation of a single node in the map layer. This not only carries information in an inefficient and unreliable way that impedes building robust multi-SOM neural architectures, but it is also inconsistent with rhythmic oscillations in biological neural networks. Here I develop and study an alternative encoding scheme that instead uses limit cycle attractors of multi-focal activity patterns to represent input patterns/sequences. Such a fundamental change in representation raises several questions: Can this be done effectively and reliably? If so, will map formation still occur? What properties would limit cycle SOMs exhibit? Could multiple such SOMs interact effectively? Could robust architectures based on such SOMs be built for practical applications? The principal results of examining these questions are as follows. First, conditions are established for limit cycle attractors to emerge in a SOM through self-organization when encoding both static and temporal sequence inputs. It is found that under appropriate conditions a set of learned limit cycles are stable, unique, and preserve input relationships. In spite of the continually changing activity in a limit cycle SOM, map formation continues to occur reliably. Next, associations between limit cycles in different SOMs are learned. It is shown that limit cycles in one SOM can be successfully retrieved by another SOM’s limit cycle activity. Control timings can be set quite arbitrarily during both training and activation. Importantly, the learned associations generalize to new inputs that have never been seen during training. Finally, a complete neural architecture based on multiple limit cycle SOMs is presented for robotic arm control. This architecture combines open-loop and closed-loop methods to achieve high accuracy and fast movements through smooth trajectories. The architecture is robust in that disrupting or damaging the system in a variety of ways does not completely destroy the system. I conclude that limit cycle SOMs have great potentials for use in constructing robust neural architectures.Item SMILE: Simulator for Maryland Imitation Learning Environment(2016-05-19) Huang, Di-Wei; Katz, Garrett E.; Gentili, Rodolphe J.; Reggia, James A.As robot imitation learning is beginning to replace conventional hand-coded approaches in programming robot behaviors, much work is focusing on learning from the actions of demonstrators. We hypothesize that in many situations, procedural tasks can be learned more effectively by observing object behaviors while completely ignoring the demonstrator's motions. To support studying this hypothesis and robot imitation learning in general, we built a software system named SMILE that is a simulated 3D environment. In this virtual environment, both a simulated robot and a user-controlled demonstrator can manipulate various objects on a tabletop. The demonstrator is not embodied in SMILE, and therefore a recorded demonstration appears as if the objects move on their own. In addition to recording demonstrations, SMILE also allows programing the simulated robot via Matlab scripts, as well as creating highly customizable objects for task scenarios via XML. This report describes the features and usages of SMILE.