The effect of multiple knowledge sources on learning and teaching

Current paradigms for machine-based learning and teaching tend to perform their task in isolation from a rich context of existing knowledge. In contrast, the research project presented here takes the view that bringing multiple sources of knowledge to bear is of central importance to learning in complex domains. As a consequence teaching must both take advantage of and beware of interactions between new and existing knowledge. The central process which connects learning to its context is reasoning by analogy, a primary concern of this research. In teaching, the connection is provided by the explicit use of a learning model to reason about the choice of teaching actions. In this learning paradigm, new concepts are incrementally refined and integrated into a body of expertise, rather than being evaluated against a static notion of correctness. The domain chosen for this experimentation is that of learning to solve "algebra story problems." A model of acquiring problem solving skills in this domain is described, including: representational structures for background knowledge, a problem solving architecture, learning mechanisms, and the role of analogies in applying existing problem solving abilities to novel problems. Examples of learning are given for representative instances of algebra story problems. After relating our views to the psychological literature, we outline the design of a teaching system. Finally, we insist on the interdependence of learning and teaching and on the synergistic effects of conducting both research efforts in parallel

The challenge of complexity for cognitive systems

Complex cognition addresses research on (a) high-level cognitive processes – mainly problem solving, reasoning, and decision making – and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods – analytical, empirical, and engineering methods – which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition – complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research

Understanding analogical reasoning : viewpoints from psychology and related disciplines

Analogy and metaphor have a long history of study in linguistics, education, philosophy and psychology. Consensus over what analogy is or how analogy functions in language and thought, however, has been elusive. This paper, the first in a two part series, examines these various research traditions, attempting to bring out major lines of agreement over the role of analogy in individual human experience. As well as being a general literature review which may be helpful for newcomers to the study of analogy, this paper attempts to extract from these literatures existing theories, models and concepts which may be interesting or useful for computational studies of analogical reasoning

How Culture comes to Mind: From Social affordances to Cultural analogies

Until now, the naturalist attempts to account for cultural phenomena have tended to see them as representations that spread within the population thanks to the counterintuitive properties making them salient and easy to remember. As a supplement to this view, which postulates a kind of cognitive distance between individuals and culture, this paper proposes a naturalist model that takes into consideration the strong cognitive involvement and the participative rather than contemplative stance triggered by a good many cultural phenomena. Such a model tries to defend a «continuist view » of the link between nature and culture by calling partially into question the traditional emphasis of social sciences on the artificial, arbitrary dimension of social facts. For the authors, indeed, this emphasis does not account for the naturality and universality of a certain number of elementary social forms. Once the partial naturality of the social is asserted, the purpose is to describe the emergence of cultural phenomena. The hypothesis put forward here is that analogical capacities, also natural, which allow human minds to «draw » cultural forms from the world of nature, either physical or social, play a central role in the elaboration of a sphere of collective experience that is both cultural and intuitive.Comment la culture vient à l'esprit. Des affordances sociales aux analogies culturelles. Jusqu’à présent, les tentatives naturalistes visant à rendre compte des phénomènes culturels ont eu tendance à les appréhender comme des représentations qui se diffusent dans la population grâce à leurs propriétés contreintuitives, qui retiennent l’attention et facilitent la mémorisation individuelle. En complément à cette perspective, qui présuppose une forme de distanciation cognitive entre les individus et leur culture, cet article propose un modèle naturaliste qui prend acte de la forte implication cognitive et de la posture, non pas contemplative mais participative, que provoquent bon nombre de phénomènes culturels. Un tel modèle tente de défendre une «vision continuiste » du lien entre nature et culture en remettant partiellement en question la focalisation traditionnelle des sciences sociales sur la dimension artificielle et arbitraire des faits sociaux. Pour les auteurs, en effet, cette focalisation ne rend pas compte de la naturalité et de l’universalité d’un certain nombre de formes sociales élémentaires. Une fois posée la naturalité partielle du social, l’objectif est alors de rendre compte de l’émergence des phénomènes culturels. L’hypothèse défendue ici est que les capacités analogiques, elles aussi naturelles, qui permettent aux esprits humains de «dériver » les formes culturelles du monde de la nature, qu’il soit physique ou social, jouent un rôle central dans l’élaboration d’une sphère de l’expérience collective qui est tout à la fois culturelle et intuitive.Clément Fabrice, Kaufmann Laurence. How Culture Comes to Mind: From Social Affordances to Cultural Analogies. In: Intellectica. Revue de l'Association pour la Recherche Cognitive, n°46-47, 2007/2-3. Culture and Society: Some Viewpoints of Cognitive Scientists. pp. 221-250

Spatial Aggregation: Theory and Applications

Visual thinking plays an important role in scientific reasoning. Based on the research in automating diverse reasoning tasks about dynamical systems, nonlinear controllers, kinematic mechanisms, and fluid motion, we have identified a style of visual thinking, imagistic reasoning. Imagistic reasoning organizes computations around image-like, analogue representations so that perceptual and symbolic operations can be brought to bear to infer structure and behavior. Programs incorporating imagistic reasoning have been shown to perform at an expert level in domains that defy current analytic or numerical methods. We have developed a computational paradigm, spatial aggregation, to unify the description of a class of imagistic problem solvers. A program written in this paradigm has the following properties. It takes a continuous field and optional objective functions as input, and produces high-level descriptions of structure, behavior, or control actions. It computes a multi-layer of intermediate representations, called spatial aggregates, by forming equivalence classes and adjacency relations. It employs a small set of generic operators such as aggregation, classification, and localization to perform bidirectional mapping between the information-rich field and successively more abstract spatial aggregates. It uses a data structure, the neighborhood graph, as a common interface to modularize computations. To illustrate our theory, we describe the computational structure of three implemented problem solvers -- KAM, MAPS, and HIPAIR --- in terms of the spatial aggregation generic operators by mixing and matching a library of commonly used routines.Comment: See http://www.jair.org/ for any accompanying file

Anchoring Knowledge in Interaction: Towards a Harmonic Subsymbolic/Symbolic Framework and Architecture of Computational Cognition

We outline a proposal for a research program leading to a new paradigm, architectural framework, and prototypical implementation, for the cognitively inspired anchoring of an agent’s learning, knowledge formation, and higher reasoning abilities in real-world interactions: Learning through interaction in real-time in a real environment triggers the incremental accumulation and repair of knowledge that leads to the formation of theories at a higher level of abstraction. The transformations at this higher level filter down and inform the learning process as part of a permanent cycle of learning through experience, higher-order deliberation, theory formation and revision. The envisioned framework will provide a precise computational theory, algorithmic descriptions, and an implementation in cyber-physical systems, addressing the lifting of action patterns from the subsymbolic to the symbolic knowledge level, effective methods for theory formation, adaptation, and evolution, the anchoring of knowledge-level objects, real-world interactions and manipulations, and the realization and evaluation of such a system in different scenarios. The expected results can provide new foundations for future agent architectures, multi-agent systems, robotics, and cognitive systems, and can facilitate a deeper understanding of the development and interaction in human-technological settings

A narrative in three acts: Using combinations of image schemas to model events

Image schemas have been proposed as conceptual building blocks corresponding to the hypothesised most fundamental embodied experiences. We formally investigate how combinations of image schemas (or 'image schematic profiles') can model essential aspects of events, and discuss benefits for artificial intelligence and cognitive systems research, in particular concerning the role of such basic events in concept formation. More specifically, as exemplary illustrations and proof of concept the image schemas Object, Contact, and Path are combined to form the events Blockage, Bouncing, and Caused-Movement. Additionally, an outline of a proposed conceptual hierarchy of levels of modelling for image schemas and similar cognitive theories is given