Plugin Framework-Based Neuro-Symbolic Grounded Task Planning for Multi-Agent System

As the roles of robots continue to expand in general, there is an increasing demand for research on automated task planning for a multi-agent system that can independently execute tasks in a wide and dynamic environment. This study introduces a plugin framework in which multiple robots can be involved in task planning in a broad range of areas by combining symbolic and connectionist approaches. The symbolic approach for understanding and learning human knowledge is useful for task planning in a wide and static environment. The network-based connectionist approach has the advantage of being able to respond to an ever-changing dynamic environment. A planning domain definition language-based planning algorithm, which is a symbolic approach, and the cooperative–competitive reinforcement learning algorithm, which is a connectionist approach, were utilized in this study. The proposed architecture is verified through a simulation. It is also verified through an experiment using 10 unmanned surface vehicles that the given tasks were successfully executed in a wide and dynamic environment.

A connectionist approach to analogy. On the modal meaning of periphrastic do in Early Modern English

This paper innovatively charts the analogical influence of the modal auxiliaries on the regulation of periphrastic do in Early Modern English by means of Convolutional Neural Networks (CNNs), a flavour of connectionist models known for their applications in computer vision. CNNs can be harnessed to model the choice between competitors in a linguistic alternation by extracting not only the contexts a construction occurs in, but also the contexts it could have occurred in, but did not. Bearing on the idea that two forms are perceived as similar if they occur in similar contexts, the models provide us with pointers towards potential loci of analogical attraction that would be hard to retrieve otherwise. Our analysis reveals clear functional overlap between do and all modals, indicating not only that analogical pressure was highly likely, but even that affirmative declarative do functioned as a modal auxiliary itself throughout the late 16th century.

The exploratory status of postconnectionist models

This paper aims to offer a new view of the role of connectionist models in the study of human cognition through the conceptualization of the history of connectionism - from the simplest perceptrons to convolutional neural nets based on deep learning techniques, as well as through the interpretation of criticism coming from symbolic cognitive science. Namely, the connectionist approach in cognitive science was the target of sharp criticism from the symbolists, which on several occasions caused its marginalization and almost complete abandonment of its assumptions in the study of cognition. Criticisms have mostly pointed to its explanatory inadequacy as a theory of cognition or to its biological implausibility as a theory of implementation, and critics often focused on specific shortcomings of some connectionist models and argued that they apply on connectionism in general. In this paper we want to show that both types of critique are based on the assumption that the only valid explanations in cognitive science are instances of homuncular functionalism and that by removing this assumption and by adopting an alternative methodology - exploratory mechanistic strategy, we can reject most objections to connectionism as irrelevant, explain the progress of connectionist models despite their shortcomings and sketch the trajectory of their future development. By adopting mechanistic explanations and by criticizing functionalism, we will reject the objections of explanatory inadequacy, by characterizing connectionist models as generic rather than concrete mechanisms, we will reject the objections of biological implausibility, and by attributing the exploratory character to connectionist models we will show that practice of generalizing current to general failures of connectionism is unjustified.

Phonological Perspective in Recognizing Kurdish Language Word

This research is confers Speech Perception and a Phonological perspective in a frame of TRACE model in a Connectionist point of view and that’s all to Analysis Kurdish and Persian language relatives. According to this view the research entitled (Speech perception and phonological Analysis in Word Recognition). In this research we assumes that Kurdish and Persian language have many similarity especially in phonological level , for reasoning this assumption and analyzing our data we use TRACE model which, developed by McClelland and Elman (1986), is speech recognition model similar to the interactive model of word recognition , and it is based on a connectionist approach in psycholinguistics. The data that this research investigate drive us to serial Conclusion which are close to our first assumption (more relative between two language ) and every difference between two related languages should be explicable to a high degree of plausibility, and systematic changes by this the study declares historical relationships between the two languages and the synchronically difference. The research content a preface and a body which is divide to three sections and a conclusion: First section: it contents a review of Trace Model (concept and the principle) and the reason to work with this model and selecting it among all another models, because TRACE is based on a connectionist system. There are connections among units at three levels: features, phonemes and words and The TRACE model are thus consistent with the idea of competition among units in the lexicon. In other words, at given time. Second section: in this section we achieve the TRACE model in Kurdish language, particularly in word cognition and phonological level because the models base on three basic levels: phonological Features, phonemes and word. Third section : we compare in this section both of the two languages(Kurdish and Persian ) through what we got from the results in the second section and this comparative lead us to three main results, like that the both languages have similar phonemic system with a rare different in phonemes feature.

A Reading Model from the Perspective of Japanese Orthography: Connectionist Approach to the Hypothesis of Granularity and Transparency

This study presents a computer simulation model of reading in Japanese syllabic kana and morphographic kanji. The model was based on the simulation model developed by Harm and Seidenberg for reading in English. The purpose of building the current model was to verify the validity of the hypothesis of granularity and transparency (HGT) postulated by Wydell and Butterworth, focusing on the granularity dimension. The HGT was developed in order to explain the behavioral dissociation between excellent reading skills in Japanese and poor reading skills in English of an English–Japanese bilingual individual as well as the relatively low incidence of developmental dyslexia in Japan. The current model was successful in simulating the granularity dimension of the HGT. The study also identified several limitations, which need to be addressed in future research.