Analogy, Amalgams, and Concept Blending

Concept blending — a cognitive process which allows for the combination of certain elements (and their relations) from originally distinct conceptual spaces into a new unified space combining these previously separate elements, and enables reasoning and inference over the combination — is taken as a key element of creative thought and combinatorial creativity. In this paper, we provide an intermediate report on work towards the development of a computational-level and algorithmic-level account of concept blending. We present the theoretical background as well as an algorithmic proposal combining techniques from computational analogy-making and case-based reasoning, and exemplify the feasibility of the approach in two case studies.. © 2015 Cognitive Systems Foundation.The authors acknowledge the financial support of the Future and Emerging Technologies programme within the Seventh Framework Programme for Research of the European Commission, under FET-Open grant number: 611553 (COINVENT)Peer Reviewe

"Why do so?" -- A Practical Perspective on Machine Learning Security

Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study with 139 industrial practitioners. We analyze attack occurrence and concern and evaluate statistical hypotheses on factors influencing threat perception and exposure. Our results shed light on real-world attacks on deployed machine learning. On the organizational level, while we find no predictors for threat exposure in our sample, the amount of implement defenses depends on exposure to threats or expected likelihood to become a target. We also provide a detailed analysis of practitioners' replies on the relevance of individual machine learning attacks, unveiling complex concerns like unreliable decision making, business information leakage, and bias introduction into models. Finally, we find that on the individual level, prior knowledge about machine learning security influences threat perception. Our work paves the way for more research about adversarial machine learning in practice, but yields also insights for regulation and auditing.Comment: under submission - 18 pages, 3 tables and 4 figures. Long version of the paper accepted at: New Frontiers of Adversarial Machine Learning@ICM

Reasoning in Non-probabilistic Uncertainty: Logic Programming and Neural Symbolic Computing as Examples

This article aims to achieve two goals: to show that probability is not the only way of dealing with uncertainty (and even more, that there are kinds of uncertainty which are for principled reasons not addressable with probabilistic means); and to provide evidence that logic-based methods can well support reasoning with uncertainty. For the latter claim, two paradigmatic examples are presented: logic programming with Kleene semantics for modelling reasoning from information in a discourse, to an interpretation of the state of affairs of the intended model, and a neural-symbolic implementation of input/output logic for dealing with uncertainty in dynamic normative contexts

Abstracts of the 2014 Brains, Minds, and Machines Summer School

A compilation of abstracts from the student projects of the 2014 Brains, Minds, and Machines Summer School, held at Woods Hole Marine Biological Lab, May 29 - June 12, 2014.This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216

Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP

During the 1980s Michie defined Machine Learning in terms of two orthogonal axes of performance: predictive accuracy and comprehensibility of generated hypotheses. Since predictive accuracy was readily measurable and comprehensibility not so, later definitions in the 1990s, such as Mitchell’s, tended to use a one-dimensional approach to Machine Learning based solely on predictive accuracy, ultimately favouring statistical over symbolic Machine Learning approaches. In this paper we provide a definition of comprehensibility of hypotheses which can be estimated using human participant trials. We present two sets of experiments testing human comprehensibility of logic programs. In the first experiment we test human comprehensibility with and without predicate invention. Results indicate comprehensibility is affected not only by the complexity of the presented program but also by the existence of anonymous predicate symbols. In the second experiment we directly test whether any state-of-the-art ILP systems are ultra-strong learners in Michie’s sense, and select the Metagol system for use in humans trials. Results show participants were not able to learn the relational concept on their own from a set of examples but they were able to apply the relational definition provided by the ILP system correctly. This implies the existence of a class of relational concepts which are hard to acquire for humans, though easy to understand given an abstract explanation. We believe improved understanding of this class could have potential relevance to contexts involving human learning, teaching and verbal interaction

Theory and Implementation of Multi-Context Systems Containing Logical and Sub-Symbolic Contexts of Reasoning

In the introductory part, we give a brief overview of the state of the art concerning multi-context systems (MCS), giving some recent examples from the literature, as well as lining out advantages and disadvantages of the different approaches. Then we propose an extension of the heterogeneous multi-context reasoning framework by G. Brewka and T. Eiter, which, in addition to logical contexts of reasoning, also incorporates sub-symbolic contexts of reasoning. The main findings concerning this topic are a simple extension of the concept of bridge rules to the sub-symbolic case and the concept of bridge rule models that allows for a straightforward enumeration of all equilibria of multi-context systems. Also a very basic, yet applicable algorithm for solving this task is presented, and our approach is illustrated with two examples from the fields of text and image classification. Moreover, after some theoretical considerations containing refinements and an expansion of the basic algorithm, we present a proof of concept implementation of an MCS, already integrating different techniques for reducing computational complexity. These techniques have been developed for this very purpose and are described and analyzed as well. The main ideas are a formalism to impose constraints on bridge rules, allowing to state dependencies between different bridge rules or sets of bridge rules, and the concept of conflicting bridge rules, which allows for the application of pruning techniques within the possible set of equilibria of the MCS. Again we illustrate our approach with three examples taken from different domains of application, having a closer look at a special purpose application of multi-context systems made for museum data completion and consistency checking. Finally possible future prospects and extensions of MCS are sketched, presenting inter alia the notion of generalized bridge rules and bridge rule inference. To conclude the thesis a comparison of our work with similar or related approaches is given