123 research outputs found
Visual style: Qualitative and context-dependent categorization
Style is an ordering principle by which to structure artifacts in a design domain. The application of a visual order entails some explicit grouping property that is both cognitively plausible and contextually dependent. Central to cognitive-contextual notions are the type of representation used in analysis and the flexibility to allow semantic interpretation. We present a model of visual style based on the concept of similarity as a qualitative context-dependent categorization. The two core components of the model are semantic feature extraction and self-organizing maps (SOMs). The model proposes a method of categorizing two-dimensional unannotated design diagrams using both low-level geometric and high-level semantic features that are automatically derived from the pictorial content of the design. The operation of the initial model, called Q-SOM, is then extended to include relevance feedback (Q-SOM:RF). The extended model can be seen as a series of sequential processing stages, in which qualitative encoding and feature extraction are followed by iterative recategorization. Categorization is achieved using an unsupervised SOM, and contextual dependencies are integrated via cluster relevance determined by the observer's feedback. The following stages are presented: initial per feature detection and extraction, selection of feature sets corresponding to different spatial ontologies, unsupervised categorization of design diagrams based on appropriate feature subsets, and integration of design context via relevance feedback. From our experiments we compare different outcomes from consecutive stages of the model. The results show that the model provides a cognitively plausible and context-dependent method for characterizing visual style in design. Copyright © 2006 Cambridge University Press
Dr. Neurosymbolic, or: How I Learned to Stop Worrying and Accept Statistics
The symbolic AI community is increasingly trying to embrace machine learning
in neuro-symbolic architectures, yet is still struggling due to cultural
barriers. To break the barrier, this rather opinionated personal memo attempts
to explain and rectify the conventions in Statistics, Machine Learning, and
Deep Learning from the viewpoint of outsiders. It provides a step-by-step
protocol for designing a machine learning system that satisfies a minimum
theoretical guarantee necessary for being taken seriously by the symbolic AI
community, i.e., it discusses "in what condition we can stop worrying and
accept statistical machine learning." Unlike most textbooks which are written
for students trying to specialize in Stat/ML/DL and willing to accept jargons,
this memo is written for experienced symbolic researchers that hear a lot of
buzz but are still uncertain and skeptical. Information on Stat/ML/DL is
currently too scattered or too noisy to invest in. This memo prioritizes
compactness, citations to old papers (many in early 20th century), and concepts
that resonate well with symbolic paradigms in order to offer time savings. It
prioritizes general mathematical modeling and does not discuss any specific
function approximator, such as neural networks (NNs), SVMs, decision trees,
etc. Finally, it is open to corrections. Consider this memo as something
similar to a blog post taking the form of a paper on Arxiv.Comment: 12 pages of main contents, 29 pages in total. It could also serve as
an accompanying material for Latplan paper. (arXiv:2107.00110) v2: rewrote
the general ELBO derivation without Prolog. v3: significantly extended the
Bayesian reasoning section in the appendix, with several proofs for conjugate
priors. v4+: errata fi
The propositional nature of human associative learning
The past 50 years have seen an accumulation of evidence suggesting that associative learning depends oil high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research
A computational framework of human causal generalization
How do people decide how general a causal relationship is, in terms of the entities or
situations it applies to? How can people make these difficult judgments in a fast, efficient
way? To address these questions, I designed a novel online experiment interface
that systematically measures how people generalize causal relationships, and developed
a computational modeling framework that combines program induction (about the hidden
causal laws) with non-parametric category inference (about their domains of influence)
to account for unique patterns in human causal generalization. In particular, by
introducing adaptor grammars to standard Bayesian-symbolic models, this framework
formalizes conceptual bootstrapping as a general online inference algorithm that gives
rise to compositional causal concepts.
Chapter 2 investigates one-shot causal generalization, where I find that participants’
inferences are shaped by the order of the generalization questions they are asked. Chapter
3 looks into few-shot cases, and finds an asymmetry in the formation of causal categories:
participants preferentially identify causal laws with features of the agent objects
rather than recipients, but this asymmetry disappears when visual cues to causal agency
are challenged. The proposed modeling approach can explain both the generalizationorder
effect and the causal asymmetry, outperforming a naïve Bayesian account while
providing a computationally plausible mechanism for real-world causal generalization.
Chapter 4 further extends this framework with adaptor grammars, using a dynamic conceptual
repertoire that is enriched over time, allowing the model to cache and later
reuse elements of earlier insights. This model predicts systematically different learned
concepts when the same evidence is processed in different orders, and across four experiments
people’s learning outcomes indeed closely resembled this model’s, differing
significantly from alternative accounts
Explaining national identity: from group attachments to collective action
This paper discusses the motivations, perceptions, and cognitions that are the foundation for group identity and stereotypes. Forming the basis for larger national identities, these attachments and categorizations are shown to be instrumental in mobilizing group members for collective action leading often to war. Drawing on literatures in social psychology, comparative politics, and international relations, an attempt is made to bridge the micro and macro levels of analysis. The research reviewed is organized into a framework that connects social-psychological processes of identity formation to inter-group conflict within and between nations. Group loyalties are connected to collective actions through the influence of public opinion, political representation, policy-making, and norms. This framework is broadened further by considering variability in a society’s political institutions, events that mark transitions in regimes or political cultures, and receptivity to appeals made by policy-making elites. The paper concludes with some implications for the resolution of conflicts between groups and nations and identifies a number of avenues for further research
Rule mining on extended knowledge graphs
Masteroppgave i informatikkINF399MAMN-PROGMAMN-IN
From Language to Motor Gavagai: Unified Imitation Learning of Multiple Linguistic and Non-linguistic Sensorimotor Skills
International audienceWe identify a strong structural similarity between the Gavagai problem in language acquisition and the problem of imitation learning of multiple context-dependent sensorimotor skills from human teachers. In both cases, a learner has to resolve concurrently multiple types of ambiguities while learning how to act in response to particular contexts through the observation of a teacher's demonstrations. We argue that computational models of language acquisition and models of motor skill learning by demonstration have so far only considered distinct subsets of these types of ambiguities, leading to the use of distinct families of techniques across two loosely connected research domains. We present a computational model, mixing concepts and techniques from these two domains, involving a simulated robot learner interacting with a human teacher. Proof-of-concept experiments show that: 1) it is possible to consider simultaneously a larger set of ambiguities than considered so far in either domain; 2) this allows us to model important aspects of language acquisition and motor learning within a single process that does not initially separate what is "linguistic" from what is "non-linguistic". Rather, the model shows that a general form of imitation learning can allow a learner to discover channels of communication used by an ambiguous teacher, thus addressing a form of abstract Gavagai problem (ambiguity about which observed behavior is "linguistic", and in that case which modality is communicative). Keywords: language acquisition, sensorimotor learning, imitation learning, motor Gavagai problem, discovering linguistic channels, robot learning by demonstration
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