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Abstract

If a solvable problem is currently unsolved, then something important to a solution is most likely being overlooked. From this simple observation we derive the obscure features hypothesis: every innovative solution is built upon at least one commonly overlooked or new (i.e., obscure) feature of the problem. By using a new definition of a feature as an effect of an interaction, we are able to accomplish five things. First, we are able to determine where features come from and how to search for new ones. Second, we are able to construct mathematical arguments that the set of features of an object is not computably enumerable. Third, we are able to characterize innovative problem solving as looking for a series of interactions that produce the desired effects (i.e., the goal). Fourth, we are able to construct a precise problem-solving grammar that is both human and machine friendly. Fifth, we are able to devise a visual and verbal problem-solving representation that both humans and computers can contribute to as they help counteract each other's problem-solving weaknesses. We show how computers can counter some of the known cognitive obstacles to innovation that humans have. We also briefly discuss ways in which humans can return the favor. We conclude that a promising process for innovative problem solving is a human–computer collaboration in which each partner assists the other in unearthing the obscure features of a problem.

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... Mccaffrey [56] developed Analogy Finder, a DbA support system to identify adaptable semantic analogies from the patent database. Later, Mccaffrey and Spector [57] devised a visual and verbal problem-solving representation to support human-machine collaboration in innovative design. Luo et al. [58] developed InnoGPS a cloud-based tool that employs an empirically-built interactive network map of all patent technology classes to guide the search for design inspiration (from patent texts) and innovation opportunities in different domains. ...
... In these human studies, full patent documents were often directly provided to designers as design aids such as prior art solutions [11,24,26,25,27,29,30], or design stimuli [15,16,17,20,28,19] for engineering design. Besides, a group of qualitative studies adopted knowledge-based rules or strategies to boost computer-aided engineering design and developed rule-based expert systems [43,31,54,68,42,47,66,33,48,57,45,62,28,13,46,41,12,35]. These expert systems usually require designers to collaborate with algorithms to tackle specific problems. ...
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... Among the different kinds of data sources for design stimuli, the patent database is probably the biggest digitized design repository. Growing work on design-byanalogy has explored the patent database and proposed various vectorization approaches for associating patent documents as design stimuli of different analogical distances to given design problems [12][13][14]. However, most of these prior works only focused on text analysis and ignored visual information. ...
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... In comparison with other computational analogy tools, such as the WordTree method (Linsey et al., 2012) andAnalogy Finder (McCaffrey andSpector, 2017), the Retriever can provide image-based stimuli or outcomes in addition to text-based outcomes. Image-based ideas or entities in mood board styles could support the comprehension of text-based outcomes, as well as improve creativity and enhance design communication. ...
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... The knowledge contained is mainly common-sense knowledge, which has limited the Retriever in constructing highly novel ontologies for supporting idea generation. Analogy Finder (McCaffrey & Spector, 2017) provide users with adaptable analogous ideas for solving technical problems by conducting searches using the US patent database. However, the tool requires the users to have strong expertise and knowledge to adapt the ideas retrieved from the US patent database employed for solving problems. ...
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... The knowledge contained is mainly common-sense knowledge, which has limited the Retriever in constructing highly novel ontologies for supporting idea generation. Analogy Finder (McCaffrey & Spector, 2017) provide users with adaptable analogous ideas for solving technical problems by conducting searches using the US patent database. However, the tool requires the users to have strong expertise and knowledge to adapt the ideas retrieved from the US patent database employed for solving problems. ...
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... The concept of the adjacent possible has now disseminated into a wide range of research fields, including economy (Kauffman 2019), innovation studies (Hochberg et al. 2017;McCaffrey and Spector 2018), technological evolution (Tria et al. 2014), cultural evolution (Gravino et al. 2016), evolution of language (Ferrer-i-Cancho 2016), biosemiotics (Favareau 2015), artistic emergence (Hoelscher 2014), design studies (Ruttonsha 2017), recommender systems (Gravino et al. 2019), learning processes (Jörg 2017), conversation analysis (Favareau 2015), and psychotherapy (Cambray 2019). Some of these studies have already been mentioned. ...
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Adjacent, i.e., nearby, possibilities constantly emerge in a multitude of settings for a multitude of entities. When these possibilities are explored, yet new possibilities emerge. The concept of the “adjacent possible” was introduced by Stuart Kauffman (1996; 2000) in evolutionary biology and complex adaptive systems to explain how biological evolution can be seen as exploration and actualization of what is adjacent possible, i.e., available at hand. The concept has now disseminated into a wide range of research fields including economy, innovation studies, technological evolution, cultural evolution, learning processes, recommender systems, and design studies. The “adjacent possible” can be defined as “the set of possibilities available to individuals, communities, institutions, organisms, productive processes, etc., at a given point in time during their evolution” (Loreto 2015, p. 9). The concept of the “adjacent possible” is useful for understanding how new possibilities emerge, and how they are constrained, discovered, explored, actualized, developed, reconfigured, designed, and so on, in an interplay between what is actual and what is possible for specific entities in specific settings. In this entry, the history and the state of art of research on the “adjacent possible” is briefly outlined. Discussion focuses on four essential aspects of “adjacent possibles”: (1) topologies of “adjacent possibles,” (2) types of “adjacent possibles,” (3) serendipity as actualization of “adjacent possibles,” and (4) designing for “adjacent possibles.”
... The knowledge base used is a hand-built semantic network based on the SBF (Structure-Behaviour-Function) modelling framework, which contains a limited amount of domain-specific knowledge regarding biological and engineering systems. Analogy Finder, developed by McCaffrey and Spector (2017), could retrieve adaptable analogues from the US patent database for solving problems. The US patent database contains useful technical knowledge from patents but was not in the form of semantic networks. ...
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... The knowledge contained is mainly practical knowledge, which has limited the Retriever in constructing profoundly new ontologies for supporting idea generation. Analogy Finder (McCaffrey & Spector, 2017) provides users with adaptable analogous ideas for solving technical problems by conducting searches using the US patent database. However, the tool requires the users to have substantial expertise and knowledge to adapt the ideas retrieved from the US patent database employed for solving problems. ...
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Purpose – to explore a crowdsourcing data-driven approach to construct crowdknowledge databases for innovation through supporting creative idea generation. In the approach, social media will be used as platforms to crowdsource knowledge for producing the databases. Findings. Creativity is an essential element of innovation, but producing creative ideas is often challenging in design. Many computational tools have become available recently to support designers in producing creative ideas that are new to individuals. As a standard feature, most of the tools rely on the databases employed, such as ConceptNet and the US Patent Database. This study highlighted that the limitations of these databases have constrained the capabilities of the tools and, thereby, new computational databases supporting the generation of new ideas to a crowd or even history are needed. Crowdsourcing outsources tasks conventionally performed in-house to a crowd and uses external knowledge to solve problems and democratize innovation. Social media is often employed in crowdsourcing for a crowd to create and share knowledge. Originality/value/scientific novelty of the research. This paper proposes a novel approach employing social media to crowdsource knowledge from a crowd for constructing crowd knowledge databases. Practical importance of the research. The crowd knowledge database is expected to be used by the current computational tools to support designers producing highly creative ideas that are new to the crowd, in new product design, and ultimately to innovation. Research limitations/Future research. In this study to provide insights and potential directions for future research are discussed that challenges of employing described approach. Paper type – theoretical.
... The work of college teachers is a kind of complex mental work that differs from that of primary and middle school teachers (Li, 2019;Kholbutaevna, 2021). Colleges must undertake complex and onerous teaching tasks, as well as more difficult and creative academic tasks (Mccaffrey and Spector, 2018;Park et al., 2021). At present, a series of reforms in the overall education system and management system has been conducted in China's higher education system to meet the needs of modern development, the internationalization of higher education, the concept of information-based learning and foreign advanced educational technology (Qian et al., 2018;Rogoza et al., 2018). ...
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Synchronized oscillators are ubiquitous in nature, and synchronization plays a key part in various classical and quantum phenomena. Several experiments have shown that in thin superconducting films, disorder enforces the droplet-like electronic texture--superconducting islands immersed into a normal matrix--and that tuning disorder drives the system from superconducting to insulating behaviour. In the vicinity of the transition, a distinct state forms: a Cooper-pair insulator, with thermally activated conductivity. It results from synchronization of the phase of the superconducting order parameter at the islands across the whole system. Here we show that at a certain finite temperature, a Cooper--air insulator undergoes a transition to a superinsulating state with infinite resistance. We present experimental evidence of this transition in titanium nitride films and show that the superinsulating state is dual to the superconducting state: it is destroyed by a sufficiently strong critical magnetic field, and breaks down at some critical voltage that is analogous to the critical current in superconductors.
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Inventors rely a lot on happenstance. Can computers help us make our own luck? Paul Marks investigates.
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The am program was constructed by Lenat in 1975 as an early experiment in getting machines to learn by discovery. In the preceding article in this issue of the AI Journal, Ritchie and Hanna focus on that work as they raise several fundamental questions about the methodology of artificial intelligence research. Part of this paper is a response to the specific points they make. It is seen that the difficulties they cite fall into four categories, the most serious of which are omitted heuristics, and the most common of which are miscommunications. Their considerations, and our post-am work on machines that learn, have clarified why am succeeded in the first place, and why it was so difficult to use the same paradigm to discover new heuristics. Those recent insights spawn questions about “where the meaning really resides” in the concepts discovered by am. This in turn leads to an appreciation of the crucial and unique role of representation in theory formation, specifically the benefits of having syntax mirror semantics. Some criticism of the paradigm of this work arises due to the ad hoc nature of many pieces of the work; at the end of this article we examine how this very adhocracy may be a potential source of power in itself.
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To study productive thinking where it is most conspicuous in great achievements is certainly a temptation, and without a doubt, important information about the genesis of productive thought could be found in biographical material. A problem arises when a living creature has a goal but does not know how this goal is to be reached. Whenever one cannot go from the given situation to the desired situation simply by action, then there has to be recourse to thinking. The subjects ( S s), who were mostly students of universities or of colleges, were given various thinking problems, with the request that they think aloud. This instruction, "Think aloud", is not identical with the instruction to introspect which has been common in experiments on thought-processes. While the introspecter makes himself as thinking the object of his attention, the subject who is thinking aloud remains immediately directed to the problem, so to speak allowing his activity to become verbal. It is the shift of function of the components of a complex mathematical pattern—a shift which must so often occur if a certain structure is to be recognized in a given pattern—it is this restructuration, more precisely: this transformation of function within a system, which causes more or less difficulty for thinking, as one individual or another tries to find a mathematical proof.
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We use our Modified Newtonian Dynamics (MOND) cosmological particle-mesh N-body code to investigate the feasibility of structure formation in a framework involving MOND and light sterile neutrinos in the mass range 11 - 300 eV: always assuming that \Omega_{\nu_s}=0.225 for H_o=72 \kms Mpc^{-1}. We run a suite of simulations with variants on the expansion history, cosmological variation of the MOND acceleration constant, different normalisations of the power spectrum of the initial perturbations and interpolating functions. Using various box sizes, but typically with ones of length 256 Mpc/h, we compare our simulated halo mass functions with observed cluster mass functions and show that (i) the sterile neutrino mass must be larger than 30 eV to account for the low mass (M_{200}<10^{14.6} solar masses) clusters of galaxies in MOND and (ii) regardless of sterile neutrino mass or any of the variations we mentioned above, it is not possible to form the correct number of high mass (M_{200}>10^{15.1} solar masses) clusters of galaxies: there is always a considerable over production. This means that the ansatz of considering the weak-field limit of MOND together with a component of light sterile neutrinos to form structure from z ~ 200 fails. If MOND is the correct description of weak-field gravitational dynamics, it could mean that subtle effects of the additional fields in covariant theories of MOND render the ansatz inaccurate, or that the gravity generated by light sterile neutrinos (or by similar hot dark matter particles) is different from that generated by the baryons.
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The observational evidence for dark matter is examined, reviewing the results of recent investigations. A historical introduction to the problem is presented, and consideration is given to single galaxies (the Galaxy; other spiral galaxies, elliptical galaxies, and dwarf galaxies); galaxies in binaries and small groups; and rich clusters, superclusters, and global aspects. Also discussed are observational constraints on the nature of dark matter, including easily excludable objects, the nucleosynthesis problem for baryons, brown and white dwarfs and black holes, nonbaryonic dark matter, and detection problems.
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In the last few years, “Design Thinking” has gained popularity – it is now seen as an exciting new paradigm for dealing with problems in sectors as far a field as IT, Business, Education and Medicine. This potential success challenges the design research community to provide unambiguous answers to two key questions: “What is the core of Design Thinking?” and “What could it bring to practitioners and organisations in other fields?”. We sketch a partial answer by considering the fundamental reasoning pattern behind design, and then looking at the core design practices of framing and frame creation. The paper ends with an exploration of the way in which these core design practices can be adopted for organisational problem solving and innovation.Highlights► Analysing design reasoning in terms of abduction. ► Presenting a framework for the description of design practices. ► Investigating the creation of new frames as a key design practice. ► Describing the different levels on which design (framing) practices can impact organisations.
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In engineering design, all products and artifacts have some intended reason behind their existence: the product or artifact function. Functional modeling provides an abstract, yet direct, method for understanding and representing an overall product or artifact function. Functional modeling also strategically guides design activities such as problem decomposition, physical modeling, product architecting, concept generation, and team organization. A formal function representation is needed to support functional modeling, and a standardized set of function-related terminology leads to repeatable and meaningful results from such a representation. We refer to this representation as a functional basis; in this paper, we seek to reconcile and integrate two independent research efforts into a significantly evolved functional basis. These efforts include research from the National Institute of Standards and Technology and two US universities, and their industrial partners. The overall approach for integrating the functional representations and the final results are presented. This approach also provides a mechanism for evaluating whether future revisions are needed to the functional basis and, if so, how to proceed. The integration process is discussed relative to differences, similarities, insights into the representations, and product validation. Based on the results, a more versatile and comprehensive design vocabulary emerges. This vocabulary will greatly enhance and expand the frontiers of research in design repositories, product architecture, design synthesis, and general product modeling.
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Research in feature-based design is reviewed. Feature-based design is regarded as a key factor towards CAD/CAPP integration from a process planning point of view. From a design point of view, feature-based design offers possibilities for supporting the design process better than current CAD systems do. The evolution of feature definitions is briefly discussed. Features and their role in the design process and as representatives of design-objects and design-object knowledge are discussed. The main research issues related to feature-based design are outlined. These are: feature representation, features and tolerances, feature validation, multiple viewpoints towards features, features and standardization, and features and languages. An overview of some academic feature-based design systems is provided. Future research issues in feature-based design are outlined. The conclusion is that feature-based design is still in its infancy, and that more research is needed for a better support of the design process and better integration with manufacturing, although major advances have already been made.
Conference Paper
Analysis of over 1,000 innovative inventions reveals that during the innovative process at least one rarely-noticed or new (i.e., obscure) feature is unearthed and built upon to create the solution (i.e., the Obscure Features Hypothesis for innovation: OFH) [6, 7]. Embedding the insights from this analysis into the structure of semantic networks creates AhaNets, which help optimize the search for the needed key obscure feature. Techniques to overcome cognitive aversions to noticing the obscure (i.e., fixation effects) further enhance innovation by improving the search process. Once implemented in software, AhaNets and counter-fixation techniques create an innovation-enhancing human-machine interaction.
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Much artificial intelligence research is based on the construction of large impressive-looking programs, the theoretical content of which may not always be clearly stated. This is unproductive from the point of view of building a stable base for further research. We illustrate this problem by referring to Lenat's AM program, in which the techniques employed are somewhat obscure in spite of the impressive performance.
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This book provides the foundation for understanding the theory and pracitce of compilers.
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Because meaningful sentences are composed of meaningful words, any system that hopes to process natural languages as people do must have information about words and their meanings. This information is traditionally provided through dictionaries, and machine-readable dictionaries are now widely available. But dictionary entries evolved for the convenience of human readers, not for machines. WordNet ¹ provides a more effective combination of traditional lexicographic information and modern computing. WordNet is an online lexical database designed for use under program control. English nouns, verbs, adjectives, and adverbs are organized into sets of synonyms, each representing a lexicalized concept. Semantic relations link the synonym sets [4].
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On 10 July 1908, in his laboratory at Leiden University, the great Dutch physicist Heike Kamerlingh Onnes (1853-1926) experienced the most glorious moment of his career. That was the day he first liquefied helium and thus opened an entirely new chapter in low-temperature physics. (See the article in PHYSICS TODAY, March 2008, page 36.)
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Traducción de: Über die spezielle und die allgemeine Relativitätstheorie
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The intent of this paper is the presentation of an associative interpretation of the process of creative thinking. The explanation is not directed to any specific field of application such as art or science but attempts to delineate processes that underlie all creative thought. The discussion will take the following form, (a) First, we will define creative thinking in associative terms and indicate three ways in which creative solutions may be achieved—serendipity, similarity, and mediation, (b) This definition will allow us to deduce those individual difference variables which will facilitate creative performance, (c) Consideration of the definition of the creative process has suggested an operational statement of the definition in the form of a test. The test will be briefly described along with some preliminary research results. (d) The paper will conclude with a discussion of predictions regarding the influence of certain experimentally manipulable variables upon the creative process. Creative individuals and the processes by which they manifest their creativity have excited a good deal of
Information-processing explanations of insight and related phenomena
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An updated historical profile of the Higgs boson
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