An agent-based model studying the acquisition of a language system of logical constructions

This paper presents an agent-based model that studies the emergence and evolution of a language system of logical constructions, i.e. a vocabulary and a set of grammatical constructions that allows the expression of logical combinations of categories. The model assumes the agents have a common vocabulary for basic categories, the ability to construct logical combinations of categories using Boolean functions, and some general purpose cognitive capacities for invention, adoption, induction and adaptation. But it does not assume the agents have a vocabulary for Boolean functions nor grammatical constructions for expressing such logical combinations of categories through language. The results of the experiments we have performed show that a language system of logical constructions emerges as a result of a process of selforganisation of the individual agents’ interactions when these agents adapt their preferences for vocabulary and grammatical constructions to those they observe are used more often by the rest of the population, and that such a language system is transmitted from one generation to the next.Peer ReviewedPostprint (published version

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe

An agent-based model of the emergence and evolution of a language system for boolean coordination

This paper presents an agent-based model of the emergence and evolution of a language system for Boolean coordination. The model assumes the agents have cognitive capacities for invention, adoption, abstraction, repair and adaptation, a common lexicon for basic concepts, and the ability to construct complex concepts using recursive combinations of basic concepts and logical operations such as negation, conjunction or disjunction. It also supposes the agents initially have neither a lexicon for logical operations nor the ability to express logical combinations of basic concepts through language. The results of the experiments we have performed show that a language system for Boolean coordination emerges as a result of a process of self-organisation of the agents' linguistic interactions when these agents adapt their preferences for vocabulary, syntactic categories and word order to those they observe are used more often by other agents. Such a language system allows the unambiguous communication of higher-order logic terms representing logical combinations of basic properties with non-trivial recursive structure, and it can be reliably transmitted across generations according to the results of our experiments. Furthermore, the conceptual and linguistic systems, and simplification and repair operations of the agent-based model proposed are more general than those defined in previous works, because they not only allow the simulation of the emergence and evolution of a language system for the Boolean coordination of basic properties, but also for the Boolean coordination of higher-order logic terms of any Boolean type which can represent the meaning of nouns, sentences, verbs, adjectives, adverbs, prepositions, prepositional phrases and subexpressions not traditionally analysed as forming constituents, using linguistic devices such as syntactic categories, word order and function words.Peer ReviewedPostprint (author's final draft

Offline and online data: on upgrading functional information to knowledge

This paper addresses the problem of upgrading functional information to knowledge. Functional information is defined as syntactically well-formed, meaningful and collectively opaque data. Its use in the formal epistemology of information theories is crucial to solve the debate on the veridical nature of information, and it represents the companion notion to standard strongly semantic information, defined as well-formed, meaningful and true data. The formal framework, on which the definitions are based, uses a contextual version of the verificationist principle of truth in order to connect functional to semantic information, avoiding Gettierization and decoupling from true informational contents. The upgrade operation from functional information uses the machinery of epistemic modalities in order to add data localization and accessibility as its main properties. We show in this way the conceptual worthiness of this notion for issues in contemporary epistemology debates, such as the explanation of knowledge process acquisition from information retrieval systems, and open data repositories

Lexically specific knowledge and individual differences in adult native speakers’ processing of the English passive

This article provides experimental evidence for the role of lexically specific representations in the processing of passive sentences and considerable education-related differences in comprehension of the passive construction. The experiment measured response time and decision accuracy of participants with high and low academic attainment using an online task that compared processing and comprehension of active and passive sentences containing verbs strongly associated with the passive and active constructions, as determined by collostructional analysis. As predicted by usage-based accounts, participants’ performance was influenced by frequency (both groups processed actives faster than passives; the low academic attainment participants also made significantly more errors on passive sentences) and lexical specificity (i.e., processing of passives was slower with verbs strongly associated with the active). Contra to proposals made by Dąbrowska and Street (2006), the results suggest that all participants have verb-specific as well as verb-general representations, but that the latter are not as entrenched in the participants with low academic attainment, resulting in less reliable performance. The results also show no evidence of a speed–accuracy trade-off, making alternative accounts of the results (e.g., those of two-stage processing models, such as Townsend & Bever, 2001) problematic

CLiFF Notes: Research In Natural Language Processing at the University of Pennsylvania

The Computational Linguistics Feedback Forum (CLIFF) is a group of students and faculty who gather once a week to discuss the members\u27 current research. As the word feedback suggests, the group\u27s purpose is the sharing of ideas. The group also promotes interdisciplinary contacts between researchers who share an interest in Cognitive Science. There is no single theme describing the research in Natural Language Processing at Penn. There is work done in CCG, Tree adjoining grammars, intonation, statistical methods, plan inference, instruction understanding, incremental interpretation, language acquisition, syntactic parsing, causal reasoning, free word order languages, ... and many other areas. With this in mind, rather than trying to summarize the varied work currently underway here at Penn, we suggest reading the following abstracts to see how the students and faculty themselves describe their work. Their abstracts illustrate the diversity of interests among the researchers, explain the areas of common interest, and describe some very interesting work in Cognitive Science. This report is a collection of abstracts from both faculty and graduate students in Computer Science, Psychology and Linguistics. We pride ourselves on the close working relations between these groups, as we believe that the communication among the different departments and the ongoing inter-departmental research not only improves the quality of our work, but makes much of that work possible

A comparison of languages which operationalise and formalise {KADS} models of expertise

In the field of Knowledge Engineering, dissatisfaction with the rapid-prototyping approach has led to a number of more principled methodologies for the construction of knowledge-based systems. Instead of immediately implementing the gathered and interpreted knowledge in a given implementation formalism according to the rapid-prototyping approach, many such methodologies centre around the notion of a conceptual model: an abstract, implementation independent description of the relevant problem solving expertise. A conceptual model should describe the task which is solved by the system and the knowledge which is required by it. Although such conceptual models have often been formulated in an informal way, recent years have seen the advent of formal and operational languages to describe such conceptual models more precisely, and operationally as a means for model evaluation. In this paper, we study a number of such formal and operational languages for specifying conceptual models. In order to enable a meaningful comparison of such languages, we focus on languages which are all aimed at the same underlying conceptual model, namely that from the KADS method for building KBS. We describe eight formal languages for KADS models of expertise, and compare these languages with respect to their modelling primitives, their semantics, their implementations and their applications. Future research issues in the area of formal and operational specification languages for KBS are identified as the result of studying these languages. The paper also contains an extensive bibliography of research in this area

Acquiring and processing verb argument structure : distributional learning in a miniature language

Adult knowledge of a language involves correctly balancing lexically-based and more language-general patterns. For example, verb argument structures may sometimes readily generalize to new verbs, yet with particular verbs may resist generalization. From the perspective of acquisition, this creates significant learnability problems, with some researchers claiming a crucial role for verb semantics in the determination of when generalization may and may not occur. Similarly, there has been debate regarding how verb-specific and more generalized constraints interact in sentence processing and on the role of semantics in this process. The current work explores these issues using artificial language learning. In three experiments using languages without semantic cues to verb distribution, we demonstrate that learners can acquire both verb-specific and verb-general patterns, based on distributional information in the linguistic input regarding each of the verbs as well as across the language as a whole. As with natural languages, these factors are shown to affect production, judgments and real-time processing. We demonstrate that learners apply a rational procedure in determining their usage of these different input statistics and conclude by suggesting that a Bayesian perspective on statistical learning may be an appropriate framework for capturing our findings

Ontology mapping: the state of the art

Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mapping has beeb the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping