Learning the Designer's Preferences to Drive Evolution

This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European Conference on the Applications of Evolutionary and bio-inspired Computation, EvoApplications 202

Bayesian Optimization for Developmental Robotics with Meta-Learning by Parameters Bounds Reduction

In robotics, methods and softwares usually require optimizations of hyperparameters in order to be efficient for specific tasks, for instance industrial bin-picking from homogeneous heaps of different objects. We present a developmental framework based on long-term memory and reasoning modules (Bayesian Optimisation, visual similarity and parameters bounds reduction) allowing a robot to use meta-learning mechanism increasing the efficiency of such continuous and constrained parameters optimizations. The new optimization, viewed as a learning for the robot, can take advantage of past experiences (stored in the episodic and procedural memories) to shrink the search space by using reduced parameters bounds computed from the best optimizations realized by the robot with similar tasks of the new one (e.g. bin-picking from an homogenous heap of a similar object, based on visual similarity of objects stored in the semantic memory). As example, we have confronted the system to the constrained optimizations of 9 continuous hyperparameters for a professional software (Kamido) in industrial robotic arm bin-picking tasks, a step that is needed each time to handle correctly new object. We used a simulator to create bin-picking tasks for 8 different objects (7 in simulation and one with real setup, without and with meta-learning with experiences coming from other similar objects) achieving goods results despite a very small optimization budget, with a better performance reached when meta-learning is used (84.3% vs 78.9% of success overall, with a small budget of 30 iterations for each optimization) for every object tested (p-value=0.036).Comment: Accepted at the IEEE International Conference on Development and Learning and Epigenetic Robotics 2020 (ICDL-Epirob 2020

Learning from Semantic Inconsistencies as the Origin of Dynamic Capabilities in MNCs: Evidence from Pharmaceutical MNCs

This paper focuses on origins of dynamic capabilities in multinational corporations (MNCs). Building on literature in the area of organizational memory and organizational learning, we investigate factors that contribute to subsidiaries of MNCs ability to detach themselves from obsolete knowledge and practices. To construct the theoretical framework, 11 extensive interviews with marketing and sales executives from three pharmaceutical MNCs operated in Iran were conducted. We test our hypotheses using statistical quantitative analysis of data related to 459 observations from subsidiaries of 51 pharmaceutical MNCs during years 2005-2009. We examine the quality of corrective actions taken by subsidiaries of pharmaceutical MNCs subsequent to subsidiaries failing to meet expected performance objectives. Our findings confirm a moderating role for internationalization, span, and the composition of human resources on the quality of corrective actions pursued

On the adequacy of current empirical evaluations of formal models of categorization

Categorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of noncomplementary models with little consensus on the relative adequacy of these accounts. Progress in assessing the relative adequacy of formal categorization models has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus)

Cognitive architectures as Lakatosian research programmes: two case studies

Cognitive architectures - task-general theories of the structure and function of the complete cognitive system - are sometimes argued to be more akin to frameworks or belief systems than scientific theories. The argument stems from the apparent non-falsifiability of existing cognitive architectures. Newell was aware of this criticism and argued that architectures should be viewed not as theories subject to Popperian falsification, but rather as Lakatosian research programs based on cumulative growth. Newell's argument is undermined because he failed to demonstrate that the development of Soar, his own candidate architecture, adhered to Lakatosian principles. This paper presents detailed case studies of the development of two cognitive architectures, Soar and ACT-R, from a Lakatosian perspective. It is demonstrated that both are broadly Lakatosian, but that in both cases there have been theoretical progressions that, according to Lakatosian criteria, are pseudo-scientific. Thus, Newell's defense of Soar as a scientific rather than pseudo-scientific theory is not supported in practice. The ACT series of architectures has fewer pseudo-scientific progressions than Soar, but it too is vulnerable to accusations of pseudo-science. From this analysis, it is argued that successive versions of theories of the human cognitive architecture must explicitly address five questions to maintain scientific credibility

From creation to consolidation: a novel framework for memory processing

Long after playing squash, your brain continues to process the events that occurred during the game, thereby improving your game, and more generally, enhancing adaptive behavior. Understanding these mysterious processes may require novel theories

The cat's cradle network

In this paper we will argue that the representation of context in knowledge management is appropriately served by the representation of the knowledge networks in an historicised form. Characterising context as essentially extra to any particular knowledge representation, we argue that another dimension to these be modelled, rather than simply elaborating a form in its own terms. We present the formalism of the cat's cradle network, and show how it can be represented by an extension of the Pathfinder associative network that includes this temporal dimension, and allows evolutions of understandings to be traced. Grounding its semantics in communities of practice ensures utility and cohesiveness, which is lost when mere externalities of a representation are communicated in fully fledged forms. The scheme is general and subsumes other formalisms for knowledge representation. The cat's cradle network enables us to model such community-based social constructs as pattern languages, shared memory and patterns of trust and reliance, by placing their establishment in a structure that shows their essential temporality

Learning to Infer Graphics Programs from Hand-Drawn Images

We introduce a model that learns to convert simple hand drawings into graphics programs written in a subset of \LaTeX. The model combines techniques from deep learning and program synthesis. We learn a convolutional neural network that proposes plausible drawing primitives that explain an image. These drawing primitives are like a trace of the set of primitive commands issued by a graphics program. We learn a model that uses program synthesis techniques to recover a graphics program from that trace. These programs have constructs like variable bindings, iterative loops, or simple kinds of conditionals. With a graphics program in hand, we can correct errors made by the deep network, measure similarity between drawings by use of similar high-level geometric structures, and extrapolate drawings. Taken together these results are a step towards agents that induce useful, human-readable programs from perceptual input

More is more in language learning:reconsidering the less-is-more hypothesis

The Less-is-More hypothesis was proposed to explain age-of-acquisition effects in first language (L1) acquisition and second language (L2) attainment. We scrutinize different renditions of the hypothesis by examining how learning outcomes are affected by (1) limited cognitive capacity, (2) reduced interference resulting from less prior knowledge, and (3) simplified language input. While there is little-to-no evidence of benefits of limited cognitive capacity, there is ample support for a More-is-More account linking enhanced capacity with better L1- and L2-learning outcomes, and reduced capacity with childhood language disorders. Instead, reduced prior knowledge (relative to adults) may afford children with greater flexibility in inductive inference; this contradicts the idea that children benefit from a more constrained hypothesis space. Finally, studies of childdirected speech (CDS) confirm benefits from less complex input at early stages, but also emphasize how greater lexical and syntactic complexity of the input confers benefits in L1-attainment