Goal Lifecycle Networks For Robotics

A Goal Lifecycle Network (GLN) is a conceptual process model that captures the progression of goals from their formulation to their completion, including planning and execution concerns. GLNs synthesize the literature on hierarchical goal networks, goal lifecycles, and plan execution. We formalize GLNs based on a state-variable representation, extend GLNs with an execution lifecycle, describe a partial reference implementation of GLNs, and show how the temporal PDDL language can be translated into GLNs for dispatchable execution. We integrate GLNs in three proof-of-concept robotics demonstrations: (1) a two-armed robot sorting items into baskets; (2) a multi-vehicle quad-rotor team surveying a region; and (3) centralized planning for a simulated disaster relief based on the Robocup Rescue League. The theory, implementation, and demonstrations highlight that GLNs are effective for goal management in the robotics systems we study.

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Modelling reading development through phonological decoding and self-teaching: implications for dyslexia

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2012.0397 ◽

2014 ◽

Vol 369 (1634) ◽

pp. 20120397 ◽

Cited By ~ 75

Author(s):

Johannes C. Ziegler ◽

Conrad Perry ◽

Marco Zorzi

Keyword(s):

Process Model ◽

Reading Development ◽

Dual Process ◽

Proof Of Concept ◽

Decoding Skills ◽

Learning To Read ◽

Phonological Decoding ◽

Orthographic Representations ◽

Teaching Implications ◽

Influential Theory

The most influential theory of learning to read is based on the idea that children rely on phonological decoding skills to learn novel words. According to the self-teaching hypothesis, each successful decoding encounter with an unfamiliar word provides an opportunity to acquire word-specific orthographic information that is the foundation of skilled word recognition. Therefore, phonological decoding acts as a self-teaching mechanism or ‘built-in teacher’. However, all previous connectionist models have learned the task of reading aloud through exposure to a very large corpus of spelling–sound pairs, where an ‘external’ teacher supplies the pronunciation of all words that should be learnt. Such a supervised training regimen is highly implausible. Here, we implement and test the developmentally plausible phonological decoding self-teaching hypothesis in the context of the connectionist dual process model. In a series of simulations, we provide a proof of concept that this mechanism works. The model was able to acquire word-specific orthographic representations for more than 25 000 words even though it started with only a small number of grapheme–phoneme correspondences. We then show how visual and phoneme deficits that are present at the outset of reading development can cause dyslexia in the course of reading development.

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What the Kalman state variable representation is good for

10.1109/cdc.1990.203815 ◽

1990 ◽

Cited By ~ 14

Author(s):

M. Fliess

Keyword(s):

State Variable ◽

Good For ◽

Variable Representation

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State variable representation of a class of linear shift-variant systems

IEEE Transactions on Acoustics Speech and Signal Processing ◽

10.1109/tassp.1982.1163978 ◽

1982 ◽

Vol 30 (6) ◽

pp. 874-880 ◽

Cited By ~ 14

Author(s):

E. Hansen ◽

A. Jablokow

Keyword(s):

State Variable ◽

Variable Representation

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Interlinking educational resources to Web of Data through IEEE LOM

Computer Science and Information Systems ◽

10.2298/csis140330088r ◽

2015 ◽

Vol 12 (1) ◽

pp. 233-255 ◽

Cited By ~ 10

Author(s):

Enayat Rajabi ◽

Miguel-Angel Sicilia ◽

Salvador Sanchez-Alonso

Keyword(s):

Linked Data ◽

Learning Object ◽

Proof Of Concept ◽

Data Types ◽

Lingua Franca ◽

Alternative Strategies ◽

Learning Object Metadata ◽

Web Of Data ◽

Reference Implementation

The emergence of Web of Data enables new opportunities for relating resources identified by URIs combined with the usage of RDF as a lingua franca for describing them. There have been to date some efforts in the direction of exposing learning object metadata following the conventions of Linked Data. However, they have not addressed an analysis on the different strategies to expose Linked Data that could be used as a basis for leveraging the metadata currently curated in repositories following common conventions and established standards. This paper describes an approach for exposing IEEE LOM metadata as Linked Data and discusses alternative strategies and their tradeoffs. The recommended approach applies common principles for Linked Data to the specificities of LOM data types and elements, identifying opportunities for interlinking exhaustively. A case study and a reference implementation along with an evaluation are also presented as a proof of concept of this mapping.

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Agent-Development Framework Based on Modular Structure to Research Disaster-Relief Activities

Robotic Systems ◽

10.4018/978-1-7998-1754-3.ch068 ◽

2020 ◽

pp. 1415-1429

Author(s):

Shunki Takami ◽

Kazuo Takayanagi ◽

Shivashish Jaishy ◽

Nobuhiro Ito ◽

Kazunori Iwata

Keyword(s):

Natural Disasters ◽

Large Scale ◽

Disaster Relief ◽

Modular Structure ◽

Program Structure ◽

Development Framework ◽

Robocup Rescue ◽

Agent Development

This article describes approaches to the RoboCup Rescue Simulation league which is a part of the response to recent large-scale natural disasters. In particular, the project provides a platform for studying disaster-relief agents and simulations. The aim of the project is to contribute to society is by making widely available the findings our research into disaster relief. Some disaster-relief agents contain excellent algorithm modules, which should ideally be shareable among developers. However, this is hindered when the program structure of the agents are different among different teams. Therefore, this article designs and implements a modular agent-development framework that unifies the structure within RoboCup Rescue Simulation agents to facilitate such technical exchange.

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