Credit assignment in multiple goal embodied visuomotor behavior

The intrinsic complexity of the brain can lead one to set aside issues related to its relationships with the body, but the field of embodied cognition emphasizes that understanding brain function at the system level requires one to address the role of the brain-body interface. It has only recently been appreciated that this interface performs huge amounts of computation that does not have to be repeated by the brain, and thus affords the brain great simplifications in its representations. In effect the brain’s abstract states can refer to coded representations of the world created by the body. But even if the brain can communicate with the world through abstractions, the severe speed limitations in its neural circuitry mean that vast amounts of indexing must be performed during development so that appropriate behavioral responses can be rapidly accessed. One way this could happen would be if the brain used a decomposition whereby behavioral primitives could be quickly accessed and combined. This realization motivates our study of independent sensorimotor task solvers, which we call modules, in directing behavior. The issue we focus on herein is how an embodied agent can learn to calibrate such individual visuomotor modules while pursuing multiple goals. The biologically plausible standard for module programming is that of reinforcement given during exploration of the environment. However this formulation contains a substantial issue when sensorimotor modules are used in combination: The credit for their overall performance must be divided amongst them. We show that this problem can be solved and that diverse task combinations are beneficial in learning and not a complication, as usually assumed. Our simulations show that fast algorithms are available that allot credit correctly and are insensitive to measurement noise

The True Lender Doctrine: Function over Form as a Reasonable Constraint on the Exportation of Interest Rates

The exportation doctrine permits national and state banks to export interest rates that are legal in one state where they operate to any other state, thereby shielding the banks from liability resulting from state usury claims. The doctrine has expanded over the last forty years to permit state and national banks to preempt a variety of state consumer-financial-protection laws. The doctrine’s high-water mark is the emergence of the “rent-a-charter” arrangement, a scheme in which a nonbank lender uses a bank as a mere conduit to originate loans that are not subject to state usury laws. This Note argues that, at minimum, nonbank entities should not be allowed the benefit of the doctrine by temporarily occupying banks for the sole purpose of originating loans that are immune from state financial consumer protection laws. A series of courts have recently begun applying a more exacting standard to these arrangements. Under the “true lender” doctrine, courts disregard the form of the lending configuration in favor of a searching examination of its substance, considering a variety of factors designed to determine which entity is the actual, rather than nominal, lender. This Note argues that the true lender doctrine’s singular focus on substance over form, combined with judicial agility to examine each factual constellation and detect any obfuscating formalities implemented by rent-a-charter parties, is presently the most effective way to sensibly limit the reach of the exportation doctrine. And, to the degree that banks assume more substantive duties in the lending process and retain some measure of risk in seeking to comply with the doctrine, the results are broadly consistent with regulatory approaches that have been deployed in the wake of the financial crisis

Intelligent control based on fuzzy logic and neural net theory

In the conception and design of intelligent systems, one promising direction involves the use of fuzzy logic and neural network theory to enhance such systems' capability to learn from experience and adapt to changes in an environment of uncertainty and imprecision. Here, an intelligent control scheme is explored by integrating these multidisciplinary techniques. A self-learning system is proposed as an intelligent controller for dynamical processes, employing a control policy which evolves and improves automatically. One key component of the intelligent system is a fuzzy logic-based system which emulates human decision making behavior. It is shown that the system can solve a fairly difficult control learning problem. Simulation results demonstrate that improved learning performance can be achieved in relation to previously described systems employing bang-bang control. The proposed system is relatively insensitive to variations in the parameters of the system environment

Teaching linguistics gotta catch ’em all: Skills grading in undergraduate linguistics

Dissatisfied with traditional grading, we developed a grading system to directly assess whether students have mastered course material. We identified the set of skills students need to master in a course and provided multiple opportunities for students to demonstrate mastery of each skill. We describe in detail how we implemented the system for two undergraduate courses, Introductory Phonetics and Phonology I. Our goals were to decrease student stress, increase student learning and make students’ study efforts more effective, increase students’ metacognitive awareness, promote a growth mindset, encourage students to aim for mastery rather than partial credit, be fairer to students facing structural and institutional disadvantages, reduce our time spent on grading, and facilitate complying with new accreditation requirements. Our own reflections and student feedback indicate that many of these goals were met

EU-Rent car rentals specification

EU-Rent is a widely known case study being promoted as a basis for demonstration of product capabilities. However, no in-depth case analysis neither specification has been developed. Therefore, it was considered interesting, useful and even necessary to develop a complete study of the case, which would lead to its whole specification. On the other hand, it was considered a good opportunity to test the application of some proposals, such as alternate mechanisms to define integrity constraints and derivation rules, as well as an alternative approach to model events.Postprint (published version

Cortical Learning of Recognition Categories: A Resolution of the Exemplar Vs. Prototype Debate

Do humans and animals learn exemplars or prototypes when they categorize objects and events in the world? How are different degrees of abstraction realized through learning by neurons in inferotemporal and prefrontal cortex? How do top-down expectations influence the course of learning? Thirty related human cognitive experiments (the 5-4 category structure) have been used to test competing views in the prototype-exemplar debate. In these experiments, during the test phase, subjects unlearn in a characteristic way items that they had learned to categorize perfectly in the training phase. Many cognitive models do not describe how an individual learns or forgets such categories through time. Adaptive Resonance Theory (ART) neural models provide such a description, and also clarify both psychological and neurobiological data. Matching of bottom-up signals with learned top-down expectations plays a key role in ART model learning. Here, an ART model is used to learn incrementally in response to 5-4 category structure stimuli. Simulation results agree with experimental data, achieving perfect categorization in training and a good match to the pattern of errors exhibited by human subjects in the testing phase. These results show how the model learns both prototypes and certain exemplars in the training phase. ART prototypes are, however, unlike the ones posited in the traditional prototype-exemplar debate. Rather, they are critical patterns of features to which a subject learns to pay attention based on past predictive success and the order in which exemplars are experienced. Perturbations of old memories by newly arriving test items generate a performance curve that closely matches the performance pattern of human subjects. The model also clarifies exemplar-based accounts of data concerning amnesia.Defense Advanced Projects Research Agency SyNaPSE program (Hewlett-Packard Company, DARPA HR0011-09-3-0001; HRL Laboratories LLC #801881-BS under HR0011-09-C-0011); Science of Learning Centers program of the National Science Foundation (NSF SBE-0354378

Overview of Polkadot and its Design Considerations

In this paper we describe the design components of the heterogenous multi-chain protocol Polkadot and explain how these components help Polkadot address some of the existing shortcomings of blockchain technologies. At present, a vast number of blockchain projects have been introduced and employed with various features that are not necessarily designed to work with each other. This makes it difficult for users to utilise a large number of applications on different blockchain projects. Moreover, with the increase in number of projects the security that each one is providing individually becomes weaker. Polkadot aims to provide a scalable and interoperable framework for multiple chains with pooled security that is achieved by the collection of components described in this paper