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Many applications in cognitive science and artificial intelligence utilize semantic similarity and relatedness to solve difficult tasks such as information retrieval, word sense disambiguation, and text classification. Previously, several approaches for evaluating concept similarity and relatedness based on WordNet or Wikipedia have been proposed. WordNet-based methods rely on highly precise knowledge but have limited lexical coverage. In contrast, Wikipedia-based models achieve more coverage but sacrifice knowledge quality. Therefore, in this paper, we focus on developing a comprehensive semantic similarity and relatedness method based on WordNet and Wikipedia. To improve the accuracy of existing measures, we combine various taxonomic and non-taxonomic features of WordNet, including gloss, lemmas, examples, sister-terms, derivations, holonyms/meronyms, and hypernyms/hyponyms, with Wikipedia gloss and hyperlinks, to describe concepts. We present a novel technique for extracting ' is-a ' and ' part-whole ' relationships between concepts using the Wikipedia link structure. The suggested technique identifies taxonomic and non-taxonomic relationships between concepts and offers dense vector representations of concepts. To fully exploit WordNet and Wikipedia's semantic attributes, the proposed method integrates their semantic knowledge at feature-level, combining semantic similarity and relatedness into a single comprehensive measure. The experimental results demonstrate the effectiveness of the proposed method over state-of-the-art measures on various gold standard benchmarks. [ABSTRACT FROM AUTHOR]

The article presents the results of a study to determine what form of personal communication persons use in different situations, telephone calls or electronic text messaging. Cognitive science researchers at Stanford University asked students which form they preferred for giving directions and for revising a paper written with colleagues. The students preferred speech for directions, due to the ability to answer questions, and text for discussing a paper because of the clarity it affords.

In this paper, we propose a neural network for ranking a given set of N numbers In 0(1/N) time. The ordering of a set of numbers based on their relative magnitudes, which is analogous to sorting,. is a fundamental operation in many algorithms. In comparison with other sorting networks reported in the literature, the,: proposed network requires fewer neurons, and fewer interconnections between neurons. The interconnections use nonlinear synapses which are composed of comparators, and do not require any weighted interconnections between neurons, as used in conventional neural networks. The proposed network has many applications, including as a component of self-organizing feature maps aid other systems where sorting is a frequent operation. [ABSTRACT FROM AUTHOR]

Flexible representations are required in order to understand and generate expert behavior. Although production rules with quantifiers can encode experiential knowledge, they often have assumptions implicit in them, making them brittle in problem scenarios where these assumptions do not hold. Qualitative models achieve flexibility by representing the domain entities and their interrelationships explicitly. However, in problem domains where assumptions underlying such models change periodically, it is necessary to be able to synthesize and maintain qualitative models in response to the changing assumptions. In this paper we argue for a representation that contains partial model components that are synthesized into qualitative models containing entities and relationships relevant to the domain. The model components can be replaced and rearranged in response to changes in the task environment. We have found this "model constructor" to be useful in synthesizing models that explain and generate expert behavior, and have explored its ability to support decision making in the problem domain of business resource planning, where reasoning is based on models that evolve in response to changing external conditions or internal policies. [ABSTRACT FROM AUTHOR]

Abstract: Industrial decision-makers often try to develop new products by reusing their past experience. Methods for obtaining feedback are available in knowledge management and produce tangible results in the industrial world. Nowadays, companies often consider employees’ knowledge as an asset. This approach is a potential answer for reusing knowledge acquired through experience. The Case-Based Reasoning (CBR) methodology, based on cognitive sciences, consists in solving new problems by reusing past experience. Case-Based Reasoning is an approach to solving a new problem by remembering and adapting a previous successful similar situation to the problem at hand. Of the four activities comprising CBR – Retrieve, Reuse, Revise, and Retain-this paper deals with the “Retrieve” phase. Consequently, we suggest using the Multi-Criteria Decisions concept in problem description to search for the solution in a case-based scenario. We show that Multi-Criteria Decisions and Case-Based Reasoning are complementary. This paper proposes using knowledge acquisition as a basis for seeking solutions from non-compensatory multi-criteria decision aids such as the ELECTRE-I and ELECTRE-II methods. We also carry out a robustness analysis at the end of this paper. We describe an industrial application with wire forming machines as an illustration of our recommendation. [Copyright &y& Elsevier]

While working on her PhD in cognitive science at University College Dublin, Birhane - who was born and raised in Ethiopia - realized no one was asking what goes into the huge data sets often used to train artificial intelligence systems. Abeba Birhane wants to make the public more aware of the real harm caused by AI bias. Birhane is on a mission to expose the biases of these data sets and hold the corporations that build and profit from them accountable. [Extracted from the article]

Abstract Based on results from cognitive science, this paper examines the two fields of three-way decision and granular computing, as well as their interplay. The ideas from one field shed new light on the other field. The integration of the two gives rise to three-way granular computing, that is, thinking, problem solving, and information processing in threes. We discuss a wide sense of three-way decision and propose a trisecting–acting–outcome (TAO) model. We explain fundamental notions of granular computing based on the philosophy of three-way decision as thinking in threes. We discuss a model of three-way granular computing by making use of two particular types of granular structures represented, respectively, by three granules and three levels. We use examples across different disciplines to demonstrate the values of the two types. Our investigation suggests that, in many situations, the power of granular computing is indeed the power of three-way decision, i.e., thinking in threes. Highlights • Discuss a wide sense of three-way decision as thinking in threes. • Propose a TAO (trisecting–acting–outcome) model of three-way decision. • Explore the interplay of three-way decision and granular computing in the context of cognitive science. • Explain granular computing according to a philosophy of thinking in threes. • Illustrate the value of three-way decision by many examples. [ABSTRACT FROM AUTHOR]

The only trouble is, when you are doing brain imaging, you are getting data from thousands of different brain areas or voxels, not just the ones we are interested in. Their book, Two Heads: Where Two Neuroscientists Explore How Our Brains Work, with Alex Frith and illustrator Daniel Locke, is out now with Bloomsbury Publishing. The caudate nucleus was practically asleep - it seems people were relying on the gossip, rather than on building up their own picture during the game. Studies scanning the brains of players while they play have found two contrasting areas showing the most activity: the caudate nucleus, and the amygdala (Rilling & Sanfey, 2011). [Extracted from the article]

Abstract: Consider the chess game: When faced with a complex scenario, how does understanding arise in one’s mind? How does one integrate disparate cues into a global, meaningful whole? How do players avoid the combinatorial explosion? How are abstract ideas represented? The purpose of this paper is to propose a new computational model of human chess cognition. We suggest that analogies and abstract roles are crucial to understanding a chess scenario. We present a proof-of-concept model, in the form of a computational architecture, which accounts for many crucial aspects of human play, such as (i) concentration of attention to relevant aspects, (ii) how humans may avoid the combinatorial explosion, (iii) perception of similarity at a strategic level, (iv) a state of meaningful anticipation over how a global scenario may evolve, and (v) the architecture’s choice as an emergent phenomenon from the actions of subcognitive processes. [Copyright &y& Elsevier]

Abstract: Effective translations between numerical and verbal representations of uncertainty are a concern shared by researchers in cognitive science and psychology, with applications to real-world risk management and decision support systems. While there is a substantial literature on such translations for point-wise probabilities, this paper contributes to the scanty literature on imprecise probability translations. Reanalysis of Budescu et al.’s [1] data on numerical interpretations of the Intergovernmental Panel on Climate Change [2] fourth report’s verbal probability expressions (PEs) revealed that negative wording has deleterious effects on lay judgements. Budescu et al. asked participants to interpret PEs in IPCC report sentences, by asking them to provide lower, “best” and upper estimates of the probabilities that they thought the authors intended. There were four experimental conditions, determining whether participants were given any numerical guidelines for translating the PEs into numbers. The first analysis focuses on twelve sentences in Budescu et al. that used the PE “very likely,” “likely,” “unlikely,” or “very unlikely”. A mixed beta regression modelling the lower, “best” and upper estimates revealed a less regressive mean and less dispersion for positive than for negative wording in all three estimates, for both the “very likely” and “likely” sentence sets. The Budescu et al. data also included a task asking for context-free translations of these PEs, and a similar pattern of results was found for that task. Negative wording therefore resulted in more regressive estimates and less consensus regardless of experimental condition. The second analysis focuses on two statements that were positive–negative duals. Appropriate pairs of responses were assessed for conjugacy and additivity. A large majority of respondents were appropriately super- and sub-additive in their lower and upper probability estimates. A mixed beta regression model of these three variables revealed that respondents were suprisingly close to obeying the conjugacy relationships for lower and upper probabilities. [Copyright &y& Elsevier]

Abstract: The problem of automatic bandwidth selection in nonparametric regression is considered when a local linear estimator is used to derive nonparametrically the unknown regression function. A plug-in method for choosing the smoothing parameter based on the use of the neural networks is presented. The method applies to dependent data generating processes with nonlinear autoregressive time series representation. The consistency of the method is shown in the paper, and a simulation study is carried out to assess the empirical performance of the procedure. [Copyright &y& Elsevier]

The article discusses issues in education, particularly the possible flaws in the teaching methods used by teachers that keep students struggling to learn. Also cited are the need to consider the findings in cognitive science to improve student learning, the experiences of teacher Eric Kalenze in teaching, and the training of teachers that focuses on teaching skills like critical thinking.

A version space is a set of all hypotheses consistent with a given set of training examples, delimited by the specific boundary and the general boundary. In existing studies [Machine Learning 17(1) (1994) 5; Proc. 5th IJCAI (1977) 305; Artificial Intelligence 18 (1982)] a hypothesis is a conjunction of attribute-value pairs, which is shown to have limited expressive power [Machine Learning, The McGraw-Hill Companies, Inc (1997)]. In a more expressive hypothesis space, e.g., disjunction of conjunction of attribute-value pairs, a general version space becomes uninteresting unless some restriction (inductive bias) is imposed [Machine Learning, The McGraw-Hill Companies, Inc (1997)].In this paper we investigate version space in a hypothesis space where a hypothesis is a hyperrelation, which is in effect a disjunction of conjunctions of disjunctions of attribute-value pairs. Such a hypothesis space is more expressive than the conjunction of attribute-value pairs and the disjunction of conjunction of attribute-value pairs. However, given a dataset, we focus our attention only on those hypotheses which are consistent with given data and are maximal in the sense that the elements in a hypothesis cannot be merged further. Such a hypothesis is called an E-set for the given data, and the set of all E-sets is the version space which is delimited by the least E-set (specific boundary) and the greatest E-set (general boundary).Based on this version space we propose three classification rules for use in different situations. The first two are based on E-sets, and the third one is based on “degraded” E-sets called weak hypotheses, where the maximality constraint is relaxed. We present an algorithm to calculate E-sets, though it is computationally expensive in the worst case. We also present an efficient algorithm to calculate weak hypotheses. The third rule is evaluated using public datasets, and the results compare well with C5.0 decision tree classifier. [Copyright &y& Elsevier]

Adaptable organisations What organisations should really be aiming for is modularity through creating systems which are adaptable in the face of uncertainty (Reeves et al., 2020). A more adaptive way of approaching an uncertain future is fostering ambivalence - holding both positive and negative feelings towards a future outcome and accepting uncertainty (Rothman et al., 2017). Optimal decisions in such areas require a kind of knowledge about future outcomes that is not possible under "radical uncertainty", where past and present data are not sufficient to anticipate the future. Scenario planning, a rather under-used tool, can do this: organisations examine how they would fare under a variety of possible futures (Laguarrigue & Ramirez, 2020). [Extracted from the article]

The article discusses the challenges involved with the evaluation of semantic similarity of concepts in areas such as artificial intelligence (AI), cognitive science, and software engineering. Such evaluation is important in the areas of digital libraries, the Semantic Web, and heterogeneous databases, the article indicates. Concept similarity measures have not been conceived that address both taxonomy and and structures, or feature vectors. The author proposes a method by which both concept taxonomy and concept structures are considered.

You have made it to the final few pages of the last New Scientist issue of the year, so it is time to test whether you have been paying attention to the science stories that truly matter. You will find the answers to all of these questions in stories we have published this year – but how many can you get without cheating? [ABSTRACT FROM AUTHOR]

The article suggests to incorporate creative thinking into the cirriculum in order to improve learning and achievement in computer science. The Generativity Theory of R. Epstein breaks creative thinking down to four core competencies: broadening, challenging, surrounding and capturing. The representation of these competencies in cognitive and neuroscience research is described. Also discussed is the integration of computational and creative thinking to form computational creativity.

Synthetic agents with varying degrees of intelligence and autonomy are being designed in many research laboratories. Motivations include military training simulations, educational software, games and entertainment, digital personal assistants, software agents managing Internet transactions or purely scientific curiosity. Different approaches are being explored, including, at one extreme, research on interactions between agents, and at the other extreme, research on processes within agents. Trade-offs between different types of architectures are not clear and much research is still required. Although more efficient toolkits dedicated to particular types of agent architectures will be more appropriate in tasks in which the architectural requirements are narrowly specified in advance, it is expected that, increasingly, toolkits with the flexibility of SIM_AGENT will be needed as research focuses strongly on agents with more sophisticated multifunctional architectures, which raise many of the hardest unsolved problems in Artificial Intelligence.

The article focuses on new technologies and applications in Robotics. While the industrial applications of robots will continue to grow, technologies for new applications of robotic systems are emerging. Robots have evolved from relatively simple programmable pick-and-place devices into a new generation of competent robot systems. Robotic sciences are becoming a discipline, and the investment in prior research has yielded a wide range of component technologies which are the foundation for the next generation of robotic systems. Naturally, the field of robotics is vast, incorporating perception, cognition, planning, and learning, and manipulation. Removing sensor-based robots from manufactured indoor environments, in which they have traditionally operated, and placing them in outdoor natural environments presents new challenges. Outdoor robots must negotiate rugged terrain, deal with objects of complex shape, and operate with natural lighting conditions. Autonomous driving requires all aspects of robotics, that is, perception, planning, mechanisms, computing, and integration.

Explores some properties that can now be expected to be inextricable from any computer superprogram that purports to represent all of human mentality. Cognitive science; Resource-realistic perspective; Indispensable features for a computer program that approximates all human mentality; Fundamental difference of the mind's program from familiar types of software.

Presents improvements made by the author on his 1971 Turning Award Lecture entitled 'Generality in Artificial Intelligence.' Approaches for achieving generality; Representing behavior by program; Background on the General Problem Solver designed to find solutions that are independent of the problem domain; Generality achieved by using the same goal-seeking mechanism for all kinds of problems; Representing knowledge in logic.

The article focuses on how people learn from visual or aural media and the design of online learning experiences for maximum retention and learning. It mentions the strengths of Instructional Design (ID) Central include thorough explanations of various instructional design models and ID process templates; and also mentions ID consists of two subsections including the cognitive science of learning, design of online learning.

In this article the author discusses aspects of topic modeling. It is explained that topic modeling is an collection of ideas drawn from computer science, mathematics, and cognitive science. The article notes that the discipline is evolving to assist users comprehend and move through a large amount of unlabeled data. A number of topics are addressed including the ability of topic models to find a hidden structure in documents, latent Dirichlet allocations (LDA), the basis for most topic models, and LDA algorithms.

The article offers a profile of philosopher Daniel Dennett. The author reflects on Dennett's focus on the creation of the human mind in cognitive science and his physicalist perspective on consciousness. Emphasis is given to topics such as the influence of science on Dennett, his atheism, and relationship with philosopher David Chalmers.

Several letters to the editor are presented in response to articles in previous issues including "Managing Prototype Knowledge/Expert System Projects," in the May 1988 issue, "Undebuggability and Cognitive Science," by Christopher Cherniak in the April 1988 issue and "On Visual Formalisms," by David Harel in the May 1988 issue.

A recently reported result concerning the global robust stability of the equilibrium point of Hopfield-type delayed neural networks is revisited. It is shown that the result is not always right. [ABSTRACT FROM AUTHOR]

The article presents information on the human-like abilities of animals discussed at the Institute for Research in Cognitive Science event held in Philadelphia, Pennsylvania. Topics tackled during the event include the moral implications for the animals, animal welfare, and the animal traits that may qualify to the personhood status.

Information about a paper discussed at a July 25, 2008 meeting sponsored by the Cognitive Science Society on the ability of English speaking people to identify small quantities while being distracted is presented. Topics include comments from Massachusetts Institute of Technology (MIT) co-study author Michael Frank and results from a similar investigation, reported in the July 19, 2008 issue of "Science News," on an Amazonian hunter-gather group, the Pirahã, by MIT's Edward Gibson.

Computerized characters that look, sound, move and seemingly think like real people are emerging from the realms of science fiction into everyday life. Making computers human is an idea as old as computers themselves, and what was initially a wild science fiction fantasy is gradually turning into fact. Much more than entertainment is at stake, of course. The potential of computerized agents or entities that are autonomous, self-directed, reactive and social — just like humans — can be estimated only in the realm of the imagination. Already, such agents have been built to present the weather on mobile phones, drive trucks, monitor environments designed to support life on other planets and perform many other sophisticated tasks. Computers are good at doing what they're told, but in this field they're required to reach their own conclusions. In the last two decades, artificial intelligence algorithms have matured to create far more sophisticated agents and researchers can now use advanced graphics technologies to create convincing characters in the form of 2D cartoons or 3D people. INSETS: Perfecting the pilots;Controlling virtual agents;Behind the wheel.

The article focuses on a microcomputer named Racter. Without the benefit of artificial intelligence, Racter quietly composes its own prose and poetry in New York's Greenwich Village. With the help of programmers William Chamberlain and Thomas Etter, Racter has actually had a collection of its poetry and prose lunacy published. Part of Racter's program does what Chamberlain calls the "bookkeeping" of English-conjugating both regular and irregular verbs, for example, and assigning number and gender to nouns. Racter chooses words from a dictionary file and rules from a syntax file to make sure those words are connected.