On Cognitive Preferences and the Plausibility of Rule-based Models

It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models, simpler models are more interpretable than more complex ones. In this position paper, we question this latter assumption by focusing on one particular aspect of interpretability, namely the plausibility of models. Roughly speaking, we equate the plausibility of a model with the likeliness that a user accepts it as an explanation for a prediction. In particular, we argue that, all other things being equal, longer explanations may be more convincing than shorter ones, and that the predominant bias for shorter models, which is typically necessary for learning powerful discriminative models, may not be suitable when it comes to user acceptance of the learned models. To that end, we first recapitulate evidence for and against this postulate, and then report the results of an evaluation in a crowd-sourcing study based on about 3.000 judgments. The results do not reveal a strong preference for simple rules, whereas we can observe a weak preference for longer rules in some domains. We then relate these results to well-known cognitive biases such as the conjunction fallacy, the representative heuristic, or the recogition heuristic, and investigate their relation to rule length and plausibility.Comment: V4: Another rewrite of section on interpretability to clarify focus on plausibility and relation to interpretability, comprehensibility, and justifiabilit

Assembling the Proofs of Ordered Model Transformations

In model-driven development, an ordered model transformation is a nested set of transformations between source and target classes, in which each transformation is governed by its own pre and post- conditions, but structurally dependent on its parent. Following the proofs-as-model-transformations approach, in this paper we consider a formalisation in Constructive Type Theory of the concepts of model and model transformation, and show how the correctness proofs of potentially large ordered model transformations can be systematically assembled from the proofs of the specifications of their parts, making them easier to derive.Comment: In Proceedings FESCA 2013, arXiv:1302.478

A comparative survey of integrated learning systems

This paper presents the duction framework for unifying the three basic forms of inference - deduction, abduction, and induction - by specifying the possible relationships and influences among them in the context of integrated learning. Special assumptive forms of inference are defined that extend the use of these inference methods, and the properties of these forms are explored. A comparison to a related inference-based learning frame work is made. Finally several existing integrated learning programs are examined in the perspective of the duction framework

Post qualifying specialist awards: approaches to enabling work-based learning in social work

All post-qualifying social work specialist awards are required to include ‘enabling the learning of others’, so that specialist social workers can provide basic support to learners in the workplace (GSCC 2006). This paper reports on a new programme unit we have designed at Bournemouth University. Our overall approach encompasses the necessary competences, but also provides for a more holistic and flexible outcome – capability. We follow the ideas of authors such as Lester (1995), Fook et al. (2000), and Barnett & Coate (2005) who show that professional development is more concerned with approaches and processes rather than fixed knowledge and outcomes. They place importance on practitioners developing the ability to identify and interpret the multifaceted nature of situations whilst considering a range of alternative options, in order to deal with the complexity and uncertainty of practice. The type of practice learning required to develop such skills, abilities and attributes is also necessarily rich in complexity. As Cheetham and Chivers (2001) point out there is no single theory of learning in the workplace and there is danger in placing too much reliance on a single approach. In accordance with the ideas above we advocate a mindset of continuing learning through critically reflective practice and collaboration with others. The presentation will discuss our learning materials and pedagogy, which aim to not only encourage and develop active, experiential, and critically reflective learning but also allow for the constructive and creative methods for the enabling of such learning. In this way we believe social workers may be educated to fully develop the professional capability or dynamic competence (Doel et al. 2002) that will enable themselves and others to function effectively in the complex world of practice. The specially written book for this unit will be distributed free to participants at the workshop

Customized Learning Sequences (CLS) by Metadata.

In response to a longterm research program for a didactical ontology, this report intends to present the results and methods for representing didactical models from the ontology we developed. The question is: How can computer technology be used to support the communication of knowledge in an educational context? This question cannot be answered by psychological experiments that ignore the core of educational behaviour: the transmission of meaning (Hönigswald 1927). Therefore this article focuses on the didactical tradition. Computer technology as a medium requires a special form of knowledge organisation, which allows learners to go individually and in a reflective way through the content (Customized Learning Sequences), thus requiring teachers to produce individually navigable hypertexts. Individualization does not mean offering "pureâ€? self-directed learning, as learning presupposes instruction by others. We have to aid teachers in reorganizing knowledge to hypertexts that allows individual navigation. Supporting learners in finding their individual path is also a crucial factor.How to aid teachers and how to set up meaningful navigation aids will be discussed in four steps:\ud 1.) Theoretical considerations; 2.) First step of Web-Didactics: Decontextualisation; 3.) Second step of Web-\ud Didactics: Recontextualisation; 4.) Research. Which theoretical considerations are eternal for Web-Didactics

CASP-DM: Context Aware Standard Process for Data Mining

We propose an extension of the Cross Industry Standard Process for Data Mining (CRISPDM) which addresses specific challenges of machine learning and data mining for context and model reuse handling. This new general context-aware process model is mapped with CRISP-DM reference model proposing some new or enhanced outputs

Distribution-based aggregation for relational learning with identifier attributes

Identifier attributes—very high-dimensional categorical attributes such as particular product ids or people’s names—rarely are incorporated in statistical modeling. However, they can play an important role in relational modeling: it may be informative to have communicated with a particular set of people or to have purchased a particular set of products. A key limitation of existing relational modeling techniques is how they aggregate bags (multisets) of values from related entities. The aggregations used by existing methods are simple summaries of the distributions of features of related entities: e.g., MEAN, MODE, SUM, or COUNT. This paper’s main contribution is the introduction of aggregation operators that capture more information about the value distributions, by storing meta-data about value distributions and referencing this meta-data when aggregating—for example by computing class-conditional distributional distances. Such aggregations are particularly important for aggregating values from high-dimensional categorical attributes, for which the simple aggregates provide little information. In the first half of the paper we provide general guidelines for designing aggregation operators, introduce the new aggregators in the context of the relational learning system ACORA (Automated Construction of Relational Attributes), and provide theoretical justification.We also conjecture special properties of identifier attributes, e.g., they proxy for unobserved attributes and for information deeper in the relationship network. In the second half of the paper we provide extensive empirical evidence that the distribution-based aggregators indeed do facilitate modeling with high-dimensional categorical attributes, and in support of the aforementioned conjectures.NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc

Towards Intelligent Databases

This article is a presentation of the objectives and techniques of deductive databases. The deductive approach to databases aims at extending with intensional definitions other database paradigms that describe applications extensionaUy. We first show how constructive specifications can be expressed with deduction rules, and how normative conditions can be defined using integrity constraints. We outline the principles of bottom-up and top-down query answering procedures and present the techniques used for integrity checking. We then argue that it is often desirable to manage with a database system not only database applications, but also specifications of system components. We present such meta-level specifications and discuss their advantages over conventional approaches