Computational Semantics

Information and meaning exist around us and within ourselves, and the same information can correspond to different meanings. This is true for humans and animals, and is becoming true for robots. We propose here an overview of this subject by using a systemic tool related to meaning generation that has already been published (C. Menant, Entropy 2003). The Meaning Generator System (MGS) is a system submitted to a constraint that generates a meaningful information when it receives an incident information that (...) has a relation with the constraint. The content of the meaningful information is explicited, and its function is to trigger an action that will be used to satisfy the constraint of the system. The MGS has been introduced in the case of basic life submitted to a "stay alive" constraint. We propose here to see how the usage of the MGS can be extended to more complex living systems, to humans and to robots by introducing new types of constraints, and integrating the MGS into higher level systems. The application of the MGS to humans is partly based on a scenario relative to the evolution of body self-awareness toward self-consciousness that has already been presented (C. Menant, Biosemiotics 2003, and TSC 2004). The application of the MGS to robots is based on the definition of the MGS applied to robots functionality, taking into account the origins of the constraints. We conclude with a summary of this overview and with themes that can be linked to this systemic approach on meaning generation. (shrink)

Information and Meaning are present everywhere around us and within ourselves. Specific studies have been implemented in order to link information and meaning: - Semiotics - Phenomenology - Analytic Philosophy - Psychology No general coverage is available for the notion of meaning. We propose to complement this lack by a systemic approach to meaning generation.

Information and Meaning are present everywhere around us and within ourselves. Specific studies have been implemented in order to link information and meaning: - Semiotics - Phenomenology - Analytic Philosophy - Psychology No general coverage is available for the notion of meaning. We propose to complement this lack by a systemic approach to meaning generation.

We propose a framework that makes space for both non-indexical contextualism and assessment-sensitivity. Such pluralism is motivated by considering possible variance in judgments about retraction. We conclude that the proposed pluralism, instead of problematizing, vindicates defining truth of a proposition w.r.t. a context of utterance and a context of assessment. To implement this formally, we formalize initialization of parameters by contexts. Then, a given parameter, depending on a speaker's judgment, can get initialized by either the context of utterance or the (...) context of assessment. An upshot of the proposal is that one need not exclusively espouse one of non-indexical contextualism or assessment-sensitivity about a particular class of expressions, for instance predicates of personal taste, in language; one can be pluralist in this sense. (This manuscript is part of an ongoing research project). (shrink)

Superposition: the 'reality' behind 'Reality.'.

Human creativity generates novel ideas to solve real-world problems. This thereby grants us the power to transform the surrounding world and extend our human attributes beyond what is currently possible. Creative ideas are not just new and unexpected, but are also successful in providing solutions that are useful, efficient and valuable. Thus, creativity optimizes the use of available resources and increases wealth. The origin of human creativity, however, is poorly understood, and semantic measures that could predict the success of generated (...) ideas are currently unknown. Here, we analyze a dataset of design problem-solving conversations in real-world settings by using 49 semantic measures based on WordNet 3.1 and demonstrate that a divergence of semantic similarity, an increased information content, and a decreased polysemy predict the success of generated ideas. The first feedback from clients also enhances information content and leads to a divergence of successful ideas in creative problem solving. These results advance cognitive science by identifying real-world processes in human problem solving that are relevant to the success of produced solutions and provide tools for real-time monitoring of problem solving, student training and skill acquisition. A selected subset of information content (IC Sánchez–Batet) and semantic similarity (Lin/Sánchez–Batet) measures, which are both statistically powerful and computationally fast, could support the development of technologies for computer-assisted enhancements of human creativity or for the implementation of creativity in machines endowed with general artificial intelligence. (shrink)

The paper introduces an extension of the proposal according to which conceptual representations in cognitive agents should be intended as heterogeneous proxytypes. The main contribution of this paper is in that it details how to reconcile, under a heterogeneous representational perspective, different theories of typicality about conceptual representation and reasoning. In particular, it provides a novel theoretical hypothesis - as well as a novel categorization algorithm called DELTA - showing how to integrate the representational and reasoning assumptions of the theory-theory (...) of concepts with the those ascribed to the prototype and exemplars-based theories. (shrink)

Half and whole articulate an always-conserved circle. Explaining symbols. Symbolic representation as a noun. Symbolic representation as a verb. (Circle as a noun and-or verb.).

During the last decades, many cognitive architectures (CAs) have been realized adopting different assumptions about the organization and the representation of their knowledge level. Some of them (e.g. SOAR [35]) adopt a classical symbolic approach, some (e.g. LEABRA[ 48]) are based on a purely connectionist model, while others (e.g. CLARION [59]) adopt a hybrid approach combining connectionist and symbolic representational levels. Additionally, some attempts (e.g. biSOAR) trying to extend the representational capacities of CAs by integrating diagrammatical representations and reasoning are (...) also available [34]. In this paper we propose a reflection on the role that Conceptual Spaces, a framework developed by Peter G¨ardenfors [24] more than fifteen years ago, can play in the current development of the Knowledge Level in Cognitive Systems and Architectures. In particular, we claim that Conceptual Spaces offer a lingua franca that allows to unify and generalize many aspects of the symbolic, sub-symbolic and diagrammatic approaches (by overcoming some of their typical problems) and to integrate them on a common ground. In doing so we extend and detail some of the arguments explored by G¨ardenfors [23] for defending the need of a conceptual, intermediate, representation level between the symbolic and the sub-symbolic one. In particular we focus on the advantages offered by Conceptual Spaces (w.r.t. symbolic and sub-symbolic approaches) in dealing with the problem of compositionality of representations based on typicality traits. Additionally, we argue that Conceptual Spaces could offer a unifying framework for interpreting many kinds of diagrammatic and analogical representations. As a consequence, their adoption could also favor the integration of diagrammatical representation and reasoning in CAs. (shrink)

In this article we present an advanced version of Dual-PECCS, a cognitively-inspired knowledge representation and reasoning system aimed at extending the capabilities of artificial systems in conceptual categorization tasks. It combines different sorts of common-sense categorization (prototypical and exemplars-based categorization) with standard monotonic categorization procedures. These different types of inferential procedures are reconciled according to the tenets coming from the dual process theory of reasoning. On the other hand, from a representational perspective, the system relies on the hypothesis of conceptual (...) structures represented as heterogeneous proxytypes. Dual-PECCS has been experimentally assessed in a task of conceptual categorization where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies, and its output has been compared to human responses. The obtained results suggest that our approach can be beneficial to improve the representational and reasoning conceptual capabilities of standard cognitive artificial systems, and –in addition– that it may be plausibly applied to different general computational models of cognition. The current version of the system, in fact, extends our previous work, in that Dual-PECCS is now integrated and tested into two cognitive architectures, ACT-R and CLARION, implementing different assumptions on the underlying invariant structures governing human cognition. Such integration allowed us to extend our previous evaluation. (shrink)

This essay is divided into two parts. In the first part (§2), I introduce the idea of practical meaning by looking at a certain kind of procedural systems — the motor system — that play a central role in computational explanations of motor behavior. I argue that in order to give a satisfactory account of the content of the representations computed by motor systems (motor commands), we need to appeal to a distinctively practical kind of meaning. Defending the explanatory relevance (...) of semantic properties in a computationalist explanation of motor behavior, my argument concludes that practical meanings play a central role in an adequate psychological theory of motor skill. In the second part of this essay (§3), I generalize and clarify the notion of practical meaning, and I defend the intelligibility of practical meanings against an important objection. (shrink)

The orbital space environment is home to natural and artificial satellites, debris, and space weather phenomena. As the population of orbital objects grows so do the potential hazards to astronauts, space infrastructure and spaceflight capability. Orbital debris, in particular, is a universal concern. This and other hazards can be minimized by improving global space situational awareness (SSA). By sharing more data and increasing observational coverage of the space environment we stand to achieve that goal, thereby making spaceflight safer and expanding (...) our knowledge of near-Earth space. To facilitate data-sharing interoperability among distinct orbital debris and space object catalogs, and SSA information systems, I proposed ontology in (Rovetto, 2015) and (Rovetto and Kelso, 2016). I continue this effort toward formal representations and models of the overall domain that may serve to improve peaceful SSA and increase our scientific knowledge. This paper explains the project concept introduced in those publications, summarizing efforts to date as well as the research field of ontology development and engineering. I describe concepts for an ontological framework for the orbital space environment, near-Earth space environment and SSA domain. An ontological framework is conceived as a part of a potential international information system. The purpose of such a system is to consolidate, analyze and reason over various sources and types of orbital and SSA data toward the mutually beneficial goals of safer space navigation and scientific research. Recent internationals findings on the limitations of orbital data, in addition to existing publications on collaborative SSA, demonstrate both the overlap with this project and the need for data-sharing and integration. (shrink)

Space situational awareness (SSA) is vital for international safety and security, and for the future of space travel. The sharing of SSA data and information should improve the state of global SSA for planetary defense and spaceflight safety. I take steps toward a Space Situational Awareness (SSA) Ontology, and outline some central objectives, requirements and desiderata in the ontology development process for this domain. The purpose of this ontological system is to explore the potential for the ontology research topic to (...) (i) represent SSA general knowledge, data, and entities/objects, (ii) clearly express the meaning of SSA data, and (iii) foster SSA data-sharing. The overall goal and motivation is to (iv) improve our capacity for planetary defense, e.g., from near- or deep-space objects and phenomena, and (v) facilitate safer and peaceful space access, navigation and travel, by improving global SSA. This research is thereby intended only for peaceful space-domain applications and uses, with particular interests in orbital debris. There is little application of ontology to the space domain as compared with other disciplines and little if any ontological development of SSA and related domains. In this respect, this paper offers novel concepts. (shrink)

Come è emerso dall’analisi filosofica e dalla ricerca nelle scienze cogni- tive, la maggior parte dei concetti, tra cui molti concetti medici, esibisce degli “effetti prototipici” e non riesce ad essere definita nei termini di condizioni necessarie e sufficienti. Questo aspetto rappresenta un problema per la pro- gettazione di ontologie in informatica, poiché i formalismi adottati per la rap- presentazione della conoscenza (a partire da OWL – Web Ontology Langua- ge) non sono in grado di rendere conto dei concetti nei (...) termini dei loro tratti prototipici. Nel presente articolo ci concentriamo sulla classe dei disordini mentali facendo riferimento alle descrizioni che ne vengono date nel DSM-5. L’idea è quella di proporre un approccio ibrido, in cui i formalismi delle ontologie sono combinati a una rappresentazione geometrica della conoscenza basata sugli spazi concettuali. (shrink)

“There’s Plenty of Room at the Bottom”, said the title of Richard Feynman’s 1959 seminal conference at the California Institute of Technology. Fifty years on, nanotechnologies have led computer scientists to pay close attention to the links between physical reality and information processing. Not all the physical requirements of optimal computation are captured by traditional models—one still largely missing is reversibility. The dynamic laws of physics are reversible at microphysical level, distinct initial states of a system leading to distinct final (...) states. On the other hand, as von Neumann already conjectured, irreversible information processing is expensive: to erase a single bit of information costs ~3 × 10−21 joules at room temperature. Information entropy is a thermodynamic cost, to be paid in non-computational energy dissipation. This paper addresses the problem drawing on Edward Fredkin’s Finite Nature hypothesis: the ultimate nature of the universe is discrete and finite, satisfying the axioms of classical, atomistic mereology. The chosen model is a cellular automaton with reversible dynamics, capable of retaining memory of the information present at the beginning of the universe. Such a CA can implement the Boolean logical operations and the other building bricks of computation: it can develop and host all-purpose computers. The model is a candidate for the realization of computational systems, capable of exploiting the resources of the physical world in an efficient way, for they can host logical circuits with negligible internal energy dissipation. (shrink)

This paper develops the ontology of space objects for theoretical and computational ontology applied to the space (astronautical/astronomical) domain. It follows “An ontological architecture for Orbital Debris Data” (Rovetto, 2015) and “Preliminaries of a Space Situational Awareness Ontology” (Rovetto, Kelso, 2016). Important considerations for developing a space object ontology, or more broadly, a space domain ontology are presented. The main category term ‘Space Object’ is analyzed from a philosophical perspective. The ontological commitments of legal definitions for artificial space objects are (...) also discussed. Space object taxonomies are offered and space object terms are defined. (shrink)

This paper applies some ontology architectures to the space domain, specifically the orbital and near-earth space environment and the space situational awareness domain. I briefly summarize local, single and hybrid ontology architectures, and offer potential space ontology architectures for each by showing how actual space data sources and space organizations would be involved.

This volume on the semantic complexity of natural language explores the question why some sentences are more difficult than others. While doing so, it lays the groundwork for extending semantic theory with computational and cognitive aspects by combining linguistics and logic with computations and cognition. -/- Quantifier expressions occur whenever we describe the world and communicate about it. Generalized quantifier theory is therefore one of the basic tools of linguistics today, studying the possible meanings and the inferential power of quantifier (...) expressions by logical means. The classic version was developed in the 1980s, at the interface of linguistics, mathematics and philosophy. Before this volume, advances in "classic" generalized quantifier theory mainly focused on logical questions and their applications to linguistics, this volume adds a computational component, the third pillar of language use and logical activity. This book is essential reading for researchers in linguistics, philosophy, cognitive science, logic, AI, and computer science. (shrink)

The orbital debris problem presents an opportunity for inter-agency and international cooperation toward the mutually beneficial goals of debris prevention, mitigation, remediation, and improved space situational awareness (SSA). Achieving these goals requires sharing orbital debris and other SSA data. Toward this, I present an ontological architecture for the orbital debris and broader SSA domain, taking steps in the creation of an orbital debris ontology (ODO). The purpose of this ontological system is to (I) represent general orbital debris and SSA domain (...) knowledge, (II) structure, and standardize where needed, orbital data and terminology, and (III) foster semantic interoperability and data-sharing. In doing so I hope to (IV) contribute to solving the orbital debris problem, improving peaceful global SSA, and ensuring safe space travel for future generations. (shrink)

In this paper a possible general framework for the representation of concepts in cognitive artificial systems and cognitive architectures is proposed. The framework is inspired by the so called proxytype theory of concepts and combines it with the heterogeneity approach to concept representations, according to which concepts do not constitute a unitary phenomenon. The contribution of the paper is twofold: on one hand, it aims at providing a novel theoretical hypothesis for the debate about concepts in cognitive sciences by providing (...) unexplored connections between different theories; on the other hand it is aimed at sketching a computational characterization of the problem of concept representation in cognitively inspired artificial systems and in cognitive architectures. (shrink)

In this small book logician and mathematician Jens Erik Fenstad addresses some of the most important foundational questions of linguistics: What should a theory of meaning look like and how might we provide the missing link between meaning theory and our knowledge of how the brain works? The author’s answer is twofold. On the one hand, he suggests that logical semantics in the Montague tradition and other broadly conceived symbolic approaches do not suffice. On the other hand, he does not (...) argue that the logical approach should be discarded; instead, he opts for a methodological pluralism in which symbolic approaches to meaning are combined with geo- metric ones such as Conceptual Spaces [9] and discusses ways in which these geometric accounts could be hooked up with connectionist frameworks and dynamic systems approaches in neurophysiology. (shrink)

Our contribution aims at individuating a valid philosophical strategy for a fruitful confrontation between human and artificial representation. The ground for this theoretical option resides in the necessity to find a solution that overcomes, on the one side, strong AI (i.e. Haugeland) and, on the other side, the view that rules out AI as explanation of human capacities (i.e. Dreyfus). We try to argue for Analytic Pragmatism (AP) as a valid strategy to present arguments for a form of weak AI (...) and to explain a notion of representation common to human and artificial agents. (shrink)

pplied ontology, philosophical ontology, biomedical ontology, artifacts, cross section, philosophy of mathematics, Phenotypic Quality Ontology, PATO , ontology of shape.

Introduction -- Basic concepts -- Previous approaches -- Semantic expressions: introduction -- Formal issues -- Semantic expressions: basic features -- Advanced features -- Applications: capture -- Three little pigs -- Applications: creation.

Understanding computation as “a process of the dynamic change of information” brings to look at the different types of computation and information. Computation of information does not exist alone by itself but is to be considered as part of a system that uses it for some given purpose. Information can be meaningless like a thunderstorm noise, it can be meaningful like an alert signal, or like the representation of a desired food. A thunderstorm noise participates to the generation of meaningful (...) information about coming rain. An alert signal has a meaning as allowing a safety constraint to be satisfied. The representation of a desired food participates to the satisfaction of some metabolic constraints for the organism. Computations on information and representations will be different in nature and in complexity as the systems that link them have different constraints to satisfy. Animals have survival constraints to satisfy. Humans have many specific constraints coming in addition. And computers will compute what the designer and programmer ask for. We propose to analyze the different relations between information, meaning and representation by taking an evolutionary approach on the systems that link them. Such a bottom-up approach allows starting with simple organisms and avoids an implicit focus on humans, which is the most complex and difficult case. To make available a common background usable for the many different cases, we use a systemic tool that defines the generation of meaningful information by and for a system submitted to a constraint [Menant, 2003]. This systemic tool allows to position information, meaning and representations for systems relatively to environmental entities in an evolutionary perspective. We begin by positioning the notions of information, meaning and representation and recall the characteristics of the Meaning Generator System (MGS) that link a system submitted to a constraint to its environment. We then use the MGS for animals and highlight the network nature of the interrelated meanings about an entity of the environment. This brings us to define the representation of an item for an agent as being the network of meanings relative to the item for the agent. Such meaningful representations embed the agents in their environments and are far from the Good Old Fashion Artificial Intelligence type ones. The MGS approach is then used for humans with a limitation resulting of the unknown nature of human consciousness. Application of the MGS to artificial systems brings to look for compatibilities with different levels of Artificial Intelligence (AI) like embodied-situated AI, the Guidance Theory of Representations, and enactive AI. Concerns relative to different types of autonomy and organic or artificial constraints are highlighted. We finish by summarizing the points addressed and by proposing some continuations. (shrink)

What are shapes? The ancient Greek geometer is perhaps the most familiar with these entities, but shape is general enough to transcend domains of inquiry. In this communication I present a broad examination of shape and related notions, such as form, boundary and surface. I aim to explore this question and describe the category of shape by identifying some of its most general features. I contrast geometric (or perfect) shapes with physical or organic shapes (the shape of objects), discuss granularity, (...) artifactuality, the cognitive aspect of shape and introduce a preliminary ontological hierarchy of shape. (shrink)

We examine the verification of simple quantifiers in natural language from a computational model perspective. We refer to previous neuropsychological investigations of the same problem and suggest extending their experimental setting. Moreover, we give some direct empirical evidence linking computational complexity predictions with cognitive reality.<br>In the empirical study we compare time needed for understanding different types of quantifiers. We show that the computational distinction between quantifiers recognized by finite-automata and push-down automata is psychologically relevant. Our research improves upon hypothesis and (...) explanatory power of recent neuroimaging studies as well as provides<br>evidence. (shrink)

We compare time needed for understanding different types of quantifiers. We show that the computational distinction between quantifiers recognized by finite-automata and pushdown automata is psychologically relevant. Our research improves upon hypothesis and explanatory power of recent neuroimaging studies as well as provides evidence for the claim that human linguistic abilities are constrained by computational complexity.

The notion of a ‘symbol’ plays an important role in the disciplines of Philosophy, Psychology, Computer Science, and Cognitive Science. However, there is comparatively little agreement on how this notion is to be understood, either between disciplines, or even within particular disciplines. This paper does not attempt to defend some putatively ‘correct’ version of the concept of a ‘symbol.’ Rather, some terminological conventions are suggested, some constraints are proposed and a taxonomy of the kinds of issue that give rise to (...) disagreement is articulated. The goal here is to provide something like a ‘geography’ of the various notions of ‘symbol’ that have appeared in the various literatures, so as to highlight the key issues and to permit the focusing of attention upon the important dimensions. In particular, the relationship between ‘tokens’ and ‘symbols’ is addressed. The issue of designation is discussed in some detail. The distinction between simple and complex symbols is clarified and an apparently necessary condition for a system to be potentially symbol, or token bearing, is introduced. (shrink)

One of the most impressive feats in robotics was the 2005 victory by a driverless Volkswagen Touareg in the DARPA Grand Challenge. This paper discusses what can be learned about the nature of representation from the car’s successful attempt to navigate the world. We review the hardware and software that it uses to interact with its environment, and describe how these techniques enable it to represent the world. We discuss robosemantics, the meaning of computational structures in robots. We argue that (...) the car constitutes a refutation of semantic arguments against the possibility of strong artificial intelligence. (shrink)

Ontology is a burgeoning field, involving researchers from the computer science, philosophy, data and software engineering, logic, linguistics, and terminology domains. Many ontology-related terms with precise meanings in one of these domains have different meanings in others. Our purpose here is to initiate a path towards disambiguation of such terms. We draw primarily on the literature of biomedical informatics, not least because the problems caused by unclear or ambiguous use of terms have been there most thoroughly addressed. We advance a (...) proposal resting on a distinction of three levels too often run together in biomedical ontology research: 1. the level of reality; 2. the level of cognitive representations of this reality; 3. the level of textual and graphical artifacts. We propose a reference terminology for ontology research and development that is designed to serve as common hub into which the several competing disciplinary terminologies can be mapped. We then justify our terminological choices through a critical treatment of the ‘concept orientation’ in biomedical terminology research. (shrink)

Dynamic logic, broadly conceived, is the logic that analyses change by decomposing actions into their basic building blocks and by describing the results of performing actions in given states of the world. The actions studied by dynamic logic can be of various kinds: actions on the memory state of a computer, actions of a moving robot in a closed world, interactions between cognitive agents performing given communication protocols, actions that change the common ground between speaker and hearer in a conversation, (...) actions that change the contextually available referents in a conversation, and so on. (shrink)

How can computers distinguish the coherent from the unintelligible, recognize new information in a sentence, or draw inferences from a natural language passage? Computational semantics is an exciting new field that seeks answers to these questions, and this volume is the first textbook wholly devoted to this growing subdiscipline. The book explains the underlying theoretical issues and fundamental techniques for computing semantic representations for fragments of natural language. This volume will be an essential text for computer scientists, linguists, and anyone (...) interested in the development of computational semantics. (shrink)

In this paper I introduce a formalism for natural language understandingbased on a computational implementation of Discourse RepresentationTheory. The formalism covers a wide variety of semantic phenomena(including scope and lexical ambiguities, anaphora and presupposition),is computationally attractive, and has a genuine inference component. Itcombines a well-established linguistic formalism (DRT) with advancedtechniques to deal with ambiguity (underspecification), and isinnovative in the use of first-order theorem proving techniques.The architecture of the formalism for natural language understandingthat I advocate consists of three levels of processing:underspecification, (...) resolution, andinference. Each of these levels has a distinct function andtherefore employs a different kind of semantic representation. Themappings between these different representations define the interfacesbetween the levels. (shrink)

In this article we discuss what constitutes a good choice of semantic representation, compare different approaches of constructing semantic representations for fragments of natural language, and give an overview of recent methods for employing inference engines for natural language understanding tasks.

In this article we discuss what constitutes a good choice of semantic representation, compare different approaches of constructing semantic representations for fragments of natural language, and give an overview of recent methods for employing inference engines for natural language understanding tasks.

In this paper, we discuss the thesis of selective representing — the idea that the contents of the mental representations had by organisms are highly constrained by the biological niches within which the organisms evolved. While such a thesis has been defended by several authors elsewhere, our primary concern here is to take up the issue of the compatibility of selective representing and realism. In this paper we hope to show three things. First, that the notion of selective representing is (...) fully consistent with the realist idea of a mind-independent world. Second, that not only are these two consistent, but that the latter provides the most powerful perspective from which to motivate and understand the differing perceptual and cog- nitive profiles themselves. And third, that the sense in which organism and environment may together constitute an integrated system of scientific interest poses no additional threat to the realist conception. (shrink)

Many recent cognitive studies reveal that human cognition is inherently perceptual, sharing systems with perception at both the conceptual and the neural levels. This paper introduces Barsalou's theory of perceptual symbols and explores its implications for philosophy of science. If perceptual symbols lie in the heart of conceptual processing, the process of attribute selection during concept representation, which is critical for defining similarity and thus for comparing taxonomies, can no longer be determined solely by background beliefs. The analogous nature of (...) perceptual symbols and the spatial nature of intraconceptual relations impose new constraints on attribute selection. These constraints help people with different background beliefs select compatible attributes, which constitute a common "platform" for taxonomy comparison. (shrink)

The processing of sequences of (English) sentences is analyzedcompositionally through transitions that merge sentences, rather thandecomposing them. Transitions that are in a precise senseinertial are related to disjunctive and non-deterministic approaches toambiguity. Modal interpretations are investigated, inducing variousequivalences on sequences.

Representationalism in cognitive science holds that semantic meaning should be explained by representations in the mind or brain. In this paper it is argued that semantic meaning should instead be explained by an abstract theory of semantic machines -- machines with predicative capability. The concept of a semantic machine (like that of a Turing machine or of Dennett's intentional systems ) is not a physical concept -- although it has physical implementations. The predicative competence of semantic machines is defined in (...) terms of independent agreement alone (cf. independent, and yet synchronised, clocks). Abstract theories are analysed as systems of quasi-apriori rules for abstract predicates. A relatively limited number of such theories and a few fundamental dimensions (space, time, mass, etc.) are today assumed to exhaust physical reality. However, that assumption need not be in conflict with predicates that cannot be defined in physical terms – for instance the functional and intentional terms that are crucial for cognitive science. (shrink)

This paper analyses the impact of representation and search operators on Computational Complexity. A model of computation is introduced based on a directed graph, and representation and search are defined to be the vertices and edges of this graph respectively. Changing either the representation or the search algorithm leads to different possible complexity classes. The final section explores the role of representation in reducing time complexity in Artificial Intelligence.

The paper gives a survey of known results related to computational devices (finite and push–down automata) recognizing monadic generalized quantifiers in finite models. Some of these results are simple reinterpretations of descriptive—feasible correspondence theorems from finite–model theory. Additionally a new result characterizing monadic quantifiers recognized by push down automata is proven.

This commentary connects some of Glenberg's ideas to similar ideas from artificial intelligence. Second, it briefly discusses hidden assumptions relating to meaning, representations, and projectable properties. Finally, questions about mechanisms, mental imagery, and conceptualization in animals are posed.