Archive for the ‘Reasoning’ Category

DataGenetics (blog)

Saturday, December 12th, 2015

DataGenetics (blog) by Nick Berry.

I mentioned Nick’s post Estimating “known unknowns” but his blog merits more than a mention of that one post.

As of today, Nick has 217 posts that touch on topics relevant to data science and have illustrations that make them memorable. You will remember those illustrations for discussions among data scientists, customers and even data science interviewers.

Follow Berry’s posts long enough and you may acquire the skill of illustrating data science ideas and problems in straight-forward prose.

Good luck!

Reasoned Programming

Tuesday, June 16th, 2015

Reasoned Programming by Krysia Broda, Susan Eisenbach, Hessam Khoshnevisan, and, Steve Vickers.

From the preface:

How do you describe what a computer program does without getting bogged down in how it does it? If the program hasn’t been written yet we can ask the same question using a different tense and slightly different wording: How do you specify what a program should do without determining exactly how it should do it? Then we can add the question: When the program is written, how do you judge that it satisfi es its specifi cation?

In civil engineering, one can ask a very similar pair of questions: How can you specify what a bridge should do without determining its design? And, when it has been designed, how can you judge whether it does indeed do what it should?

This book is about these questions for software engineering, and its answers can usefully be compared with what happens in civil engineering. First, a speci fication is a different kind of thing from a design; the specifi cation of a bridge may talk about loadbearing capacity, deflection under high winds and resistance of piers to water erosion, while the design talks about quite different things such as structural components and their assembly. For software, too, speci fications talk about external matters and programs talk about internal matters.

The second of the two questions is about judging that one thing satisfi es another. The main message of the book and a vitally important one is that judgement relies upon understanding.  This is obviously true in the case of the bridge; the judgement that the bridge can bear the speci fied load rests on structural properties of components enshrined in engineering principles, which
in turn rest upon the science of materials. Thus the judgement rests upon a tower of understanding.

This tower is well-established for the older engineering disciplines; for software engineering it is still being built. (We may call it software science.’) The authors have undertaken to tell students in their fi rst or second year about the tower as it now stands, rather than dictate principles to them. This is refreshing in software engineering there has been a tendency to substitute formality for understanding. Since a program is written in a very formal language and the specifi cation is also often written in formal logical terms, it is natural to emphasize formality in making the judgement that one satifis es the other. But in teaching it is stultifying to formalize before understanding, and software science is no exception — even if the industrial signi ficance of a formal veri fication is increasingly being recognized.

This book is therefore very approachable. It makes the interplay between speci fication and programming into a human and flexible one, albeit guided by rigour. After a gentle introduction, it treats three or four good-sized examples, big enough to give con fidence that the approach will scale up to industrial software; at the same time, there is a spirit of scienti c enquiry. The authors have made the book self-contained by including an introduction to logic written in the same spirit. They have tempered their care for accuracy with a light style of writing and an enthusiasm which I believe will endear the book to students.

Apologies for the long quote but I like the style of the preface. 😉

As you may guess from the date, 1994, the authors focus on functional programming, Miranda, and Modula-2.

Great read and highly recommended.

I first saw this in a tweet by Computer Science.

Can recursive neural tensor networks learn logical reasoning?

Thursday, March 19th, 2015

Can recursive neural tensor networks learn logical reasoning? by Samuel R. Bowman.


Recursive neural network models and their accompanying vector representations for words have seen success in an array of increasingly semantically sophisticated tasks, but almost nothing is known about their ability to accurately capture the aspects of linguistic meaning that are necessary for interpretation or reasoning. To evaluate this, I train a recursive model on a new corpus of constructed examples of logical reasoning in short sentences, like the inference of “some animal walks” from “some dog walks” or “some cat walks,” given that dogs and cats are animals. This model learns representations that generalize well to new types of reasoning pattern in all but a few cases, a result which is promising for the ability of learned representation models to capture logical reasoning.

From the introduction:

Natural language inference (NLI), the ability to reason about the truth of a statement on the basis of some premise, is among the clearest examples of a task that requires comprehensive and accurate natural language understanding [6].

I stumbled over that line in Samuel’s introduction because it implies, at least to me, that there is a notion of truth that resides outside of ourselves as speakers and hearers.

Take his first example:

Consider the statement all dogs bark. From this, one can infer quite a number of other things. One can replace the first argument of all (the first of the two predicates following it, here dogs) with any more specific category that contains only dogs and get a valid inference: all puppies bark; all collies bark.

Contrast that with one the premises that starts my day:

All governmental statements are lies of omission or commission.

Yet, firmly holding that as a “fact” of the world, I write to government officials, post ranty blog posts about government policies, urge others to attempt to persuade government to take certain positions.

Or as Leonard Cohen would say:

Everybody knows that the dice are loaded

Everybody rolls with their fingers crossed

It’s not that I think Samuel is incorrect about monotonicity for “logical reasoning” but monotonicity is a far cry from how people reason day to day.

Rather than creating “reasoning” that is such a departure from human inference, why not train a deep learning system to “reason” by exposing it to the same inputs and decisions made by human decision makers? Imitation doesn’t require understanding of human “reasoning,” just the ability to engage in the same behavior under similar circumstances.

That would reframe Samuel’s question to read: Can recursive neural tensor networks learn human reasoning?

I first saw this in a tweet by Sharon L. Bolding.

…Bad Arguments

Saturday, December 28th, 2013

An Illustrated Book of Bad Arguments by Ali Almossawi.

From “Who is this book for?”

This book is aimed at newcomers to the field of logical reasoning, particularly those who, to borrow a phrase from Pascal, are so made that they understand best through visuals. I have selected a small set of common errors in reasoning and visualized them using memorable illustrations that are supplemented with lots of examples. The hope is that the reader will learn from these pages some of the most common pitfalls in arguments and be able to identify and avoid them in practice.

A delightfully written and illustrated book on bad arguments.

I first saw this at “Bad Arguments” (a book by Ali Almossawi) by Deborah Mayo.

Proceedings of the RuleML2012@ECAI Challenge

Tuesday, September 4th, 2012

Proceedings of the RuleML2012@ECAI Challenge

The paper I mentioned on yesterday: Legal Rules, Text and Ontologies Over Time [The eternal “now?”] is part of these proceedings.

Which is a very good paper.

You will also find “reasoning” about complex tax transactions, such as seeking reimbursement from the government for taxes you have not paid. (What’s complex about that I cannot say. Merely reporting the description of: Missing Trader Fraud given in one of the papers. The taxes reported lost every year remind me of RIAA estimates on piracy.

And papers that fall in between.

Reasoning with the Variation Ontology using Apache Jena #OWL #RDF

Monday, August 27th, 2012

Reasoning with the Variation Ontology using Apache Jena #OWL #RDF by Pierre Lindenbaum.

From the post:

The Variation Ontology (VariO), “is an ontology for standardized, systematic description of effects, consequences and mechanisms of variations”.

In this post I will use the Apache Jena library for RDF to load this ontology. It will then be used to extract a set of variations that are a sub-class of a given class of Variation.

If you are interested in this example, you may also be interested in the Variation Ontology.

The VariO homepage reports:

VariO allows

  • consistent naming
  • annotation of variation effects
  • data integration
  • comparison of variations and datasets
  • statistical studies
  • development of sofware tools

It isn’t clear on a quick read, how VariO accomplishes:

  • data integration
  • comparison of variations and datasets

Unless it means uniform recordation using VariO enables “data integration,” and “comparison of variations and datasets?”

True but what nomenclature, uniformly used, does not enable “data integration,” and “comparison of variations and datasets?”

Is there one?

..the reasoning that people actually engage in

Wednesday, June 13th, 2012

Informal Logic: Reasoning and Argumentation in Theory and Practice

A self-description of the journal appears in the first issue, July of 1978:

However, as we found out at the Windsor Symposium, informal logic means many things to many people. Let us then declare our conception of it. For the time being, we shall use this term to denote a wide spectrum of interests and questions, whose only common link may appear to be that they do not readily lend themselves to treatment in the pages of “The Journal of Symbolic Logic.” More positively, we think of informal logic as covering the gamut of theoretical and practical issues that come into focus when one examines closely, from a normative viewpoint, the reasoning that people actually engage in. Subtract from this the exclusively formal issues and what remains is informal logic. Thus our conception is very broad and liberal, and covers everything from theoretical issues (theory of fallacy and argument) to practical ones (such as how best to display the structure of ordinary arguments) to pedagogical questions (how to design critical thinking courses; what sorts of material to use). [I changed the underlining of “The Journal of Sybolic Logic” to quotes to avoid confusion with hyperlinking. Emphasis added.]

“…the reasoning that people actually engage in” sounds like it would interest topic map authors.

Jack Park forwarded this to my attention.