The Rational Speech Act Framework

In the experiment described in the paper Noah Goodman & Andreas Stuhlmüller. 2013. Knowledge and im-plicature: Modeling language understanding as social cognition.Topics in Cognitive Science5(1). 173–184, empirical support was provided for the predictive power of the Rational Speech Act (RSA) model concerning the interpretation of utterances employing numerals in uncertainty contexts. The RSA predicts a Bayesian interdependence between beliefs about the probability distribution of the occurrence of an event prior to receiving information and the updated probability distribution after receiving information. In this paper we analyze whether the RSA is a descriptive or a normative model. We present the results of two analogous experiments carried out in Polish. The first experiment does not replicate the original empirical results. We find that this is due to different answers on the prior probability distribution. However, the model predicts the different results on the basis of different collected priors: Bayesian updating predicts human reasoning. By contrast, the second experiment, where the answers on the prior probability distribution are as predicted, is a replication of the original results. In light of these results we conclude that the RSA is a robust, descriptive model, however, the experimental assumptions pertaining to the experimental setting adopted by Goodman and Stuhlmüller are normative.

We combine a neural image captioner with a Rational Speech Acts (RSA) model to make a system that is pragmatically informative: its objective is to produce captions that are not merely true but also distinguish their inputs from similar images. Previous attempts to combine RSA with neural image captioning require an inference which normalizes over the entire set of possible utterances. This poses a serious problem of efficiency, previously solved by sampling a small subset of possible utterances. We instead solve this problem by implementing a version of RSA which operates at the level of characters (“a”, “b”, “c”, ...) during the unrolling of the caption. We find that the utterance-level effect of referential captions can be obtained with only character-level decisions. Finally, we introduce an automatic method for testing the performance of pragmatic speaker models, and show that our model outperforms a non-pragmatic baseline as well as a word-level RSA captioner.

Semantics is the meaning of symbols, notations, concepts, functions, and behaviors, as well as their relations that can be deduced onto a set of predefined entities and/or known concepts. Semantic computing is an emerging computational methodology that models and implements computational structures and behaviors at semantic or knowledge level beyond that of symbolic data. In semantic computing, formal semantics can be classified into the categories of to be, to have, and to do semantics. This paper presents a comprehensive survey of formal and cognitive semantics for semantic computing in the fields of computational linguistics, software science, computational intelligence, cognitive computing, and denotational mathematics. A set of novel formal semantics, such as deductive semantics, concept-algebra-based semantics, and visual semantics, is introduced that forms a theoretical and cognitive foundation for semantic computing. Applications of formal semantics in semantic computing are presented in case studies on semantic cognition of natural languages, semantic analyses of computing behaviors, behavioral semantics of human cognitive processes, and visual semantic algebra for image and visual object manipulations.

Cognitive Computing promises to fundamentally transform corporate information processing and problem solving. Building on latest advances in cognitive, data, and computer science, Cognitive Computing aims to deliver autonomous reasoning and continuous learning under consideration of contextual insights and the natural interaction of humans and machines. Cognitive Computing is expected to offer significant application opportunities for business process management (BPM). While first studies have investigated the potential impact of Cognitive Computing on BPM, the intersection between both disciplines remains largely unexplored. In particular, little work has been done on identifying Cognitive BPM use cases. To address this gap, we develop an analysis framework that aims to assist researchers and practitioners in the development of Cognitive BPM use case ideas. This framework combines the most significant problem classes addressed by Cognitive Computing with central activities of the BPM lifecycle. We also used the framework as foundation of explorative workshops and report on the most interesting cognitive BPM use cases ideas we discovered.

What counts as a philosophical issue in computational cognitive science? This chapter briefly reviews possible answers before focusing on a specific subset of philosophical issues. These surround challenges that have been raised by philosophers regarding the scope of computational models of cognition. The arguments suggest that there are aspects of human cognition that may, for various reasons, resist explanation or description in terms of computation. The primary targets of these “no go” arguments have been semantic content, phenomenal consciousness, and central reasoning. This chapter reviews the arguments and considers possible replies. It concludes by highlighting the differences between the arguments, their limitations, and how they might contribute to the wider project of estimating the value of ongoing research programs in computational cognitive science.

Cognitive Computing (CC) is a contemporary field of studies on intelligent computing methodologies and brain-inspired mechanisms of cognitive systems, cognitive machine learning and cognitive robotics. The IEEE conference ICCI*CC'17 on Cognitive Informatics and Cognitive Computing was focused on the theme of neurocomputation, cognitive machine learning and brain-inspired systems. This article reports the plenary panel (Part II) in IEEE ICCI*CC'17 at Oxford University. The summary is contributed by distinguished panelists who are part of the world's renowned scholars in the transdisciplinary field of cognitive computing.

The paper introduces a new issue in semantic processing as necessary for cognitive computing - recognizing and resolving referential ambiguity. Depending on salience and scope, often irresponsibly dismissed as unmanageable "context," the same noun or verb phrase may refer to an individual unique occurrence of an object, event, or property, all of them, or any subset. The only notion that can cross from language to reality is that of reference that is largely ignored in linguistics and sort of handled somehow in the philosophy of language. After a careful review of reference in the literature, the paper "salvages" it for cognitive computing by outlining its new functionalities, including the computation of salience and scope for referential disambiguation. It becomes a tool for representing and computing human knowledge of the world.

The contemporary wonder of sciences and engineering has recently refocused on the starting point of them: How the brain processes internal and external information autonomously and cognitively rather than imperatively as those of conventional computers do? This leads to the advances in the field of Cognitive Informatics (CI) as a transdisciplinary enquiry of computer science, information sciences, cognitive science, and intelligence science. CI investigates into the internal information processing mechanisms and processes of the brain and natural intelligence, as well as their engineering applications in cognitive computing. This paper reports a set of nine position statements presented in the plenary panel of IEEE ICCI*CC'11 on Cognitive Informatics in Year 10 and Beyond contributed from invited panelists who are part of the world's renowned researchers and scholars in the field of cognitive informatics and cognitive computing.

The notions of psychological similarity and probabilistic learning are key posits in cognitive, computational, and developmental psychology and in machine learning. However, their explanatory relationship is rarely made explicit within and across these research fields. This opinionated review critically evaluates how these notions can mutually inform each other within computational cognitive science. Using probabilistic models of concept learning as a case study, I argue that two notions of psychological similarity offer important normative constraints to guide modelers' interpretations of representational primitives. In particular, the two notions furnish probabilistic models of cognition with meaningful interpretations of what the associated subjective probabilities in the model represent and how they attach to experiences from which the agent learns. Similarity representations thereby provide probabilistic models with cognitive, as opposed to purely mathematical, content.

Bridging the gap between theory and practice of approximate Bayesian inference

In a case study of the growth of cognitive science, we analyzed the activities of the Cognitive Science Society with a particular emphasis on the multidisciplinary nature of the field. Analyses of departmental affiliations, training back‐grounds, research methodology, and paper citations suggest that the journal Cognitive Science and the Annual Meeting of the Cognitive Science Society are dominated by cognitive psychology and computer science, rather than being an equal division among the constituent disciplines of cognitive science. However, at many levels, a growing percentage of work was found to involve a conjunction of multiple disciplines, such that approximately 30–50% of recent work in the Cognitive Science Society is multidisciplinary. In a questionnaire study of cognitive scientists involved in collaborative research, multidisciplinarity was found to shape the research process and affect the factors associated with successful research.

Cognitive Informatics is a cutting-edge and multidisciplinary research area that tackles the fundamental problems shared by modern informatics, computing, software engineering, AI, cybernetics, cognitive science, neuropsychology, medical science, systems science, philosophy, linguistics, economics, management science, and life sciences. This editorial introduces the emerging field of cognitive informatics and its applications in cognitive computing, abstract intelligence, computational mathematics, and computational intelligence. The themes and structure of this special issue on cognitive informatics are described, and then, focuses of the selected papers in Part II of this special issue are highlighted.

Semantic information is usually supposed to satisfy the veridicality thesis: p qualifies as semantic information only if p is true.However, what it means for semantic information to be true is often left implicit, with correspondentist interpretations representing the most popular, default option.The article develops an alternative approach, namely a correctness theory of truth (CTT) for semantic information.This is meant as a contribution not only to the philosophy of information but also to the philosophical debate on the nature of truth.After the introduction, in section two, semantic information is shown to be translatable into propositional semantic information (i).In section three, i is polarised into a query (Q) and a result (R), qualified by a specific context, a level of abstraction and a purpose.This polarization is normalised in section four, where [Q + R] is transformed into a Boolean question and its relative yes/no answer [Q + A].This completes the reduction of the truth of i to the correctness of A. In sections five and six, it is argued that (1) A is the correct answer to Q if and only if (2) A correctly saturates Q by verifying and validating it (in the computer science's sense of "verification" and "validation"); that (2) is the case if and only if (3) [Q + A] generates an adequate model (m) of the relevant system (s) identified by Q; that (3) is the case if and only if (4) m is a proxy of s (in the computer science's sense of "proxy") and (5) proximal access to m commutes with the distal access to s (in the category theory's sense of "commutation"); and that (5) is the case if and only if (6) reading/writing (accessing, in the computer science's technical sense of the term) m enables one to read/write (access) s.Section seven provides some further clarifications about CTT, in the light of semantic paradoxes.Section eight draws a general conclusion about the nature of CTT as a theory for systems designers not just systems users.In the course of the article all technical expressions from computer science are explained.

Degrees of Belief is a strong collection of essays distinguished by its inclusion of an unusually wide array of approaches to understanding its subject. The book consists mainly of recent works (growing out of a 2004 conference at the University of Konstanz) from perspectives as diverse as probabilism (the view that degrees of belief ought to be probabilities), Dempster-Shafer theory, possibility theory, ranking theory, AGM (Alchourron-Gardenfors-Makinson) belief-revision theory, and others. The essays are varied in focus, but many are concerned with outlining formal axiomatizations of degree of belief (variously understood), and several of them are surveys of their respective sub-fields. As one might expect, the discussion is often technical and sometimes dense, and so the book is best suited for formally proficient advanced students (graduate or upper-level undergraduate) or professionals seeking to explore other approaches to degree of belief than the ones in which they currently work. Some recurrent themes in the book include the relation of degree of belief to full belief, how degree of belief ought best to be understood (its purpose and function), and the formal laws (so understood) it ought to obey. There is limited reference in some articles to applications (e.g., in computer science), but the main focus of the book is theoretical. What follows is only a small sample of the rich content contained in the book. After a clear and concise preview of the book’s contents, written by one of the editors (Franz Huber), Part I of the book concentrates on how belief relates to degree of belief. The lottery paradox is key here (and also the related preface paradox), and accordingly, the section begins with one exposition of it, by Richard Foley (a revision of Chapter 4 of Foley 1993). The lottery paradox arises from the so-called Lockean thesis that belief is just sufficiently high degree of belief. For example, if I believe p just in case my degree of belief in it is at least 99%, then I’ll

Understanding how people tick is an endeavour that has challenged us for millennia, both in informal settings and in increasingly formalized and scientific disciplines. Some are interested in the biology and others the behaviour. Some are focussed on language and others on culture or emotion. Some approach this as pure science worthy of understanding in its own right. Some approach it as applied science with value born of practical applications that improve our lifestyle and characterize our modern technological society.