Abstract Models and Cognitive Mismatch in Formal Verification

We present ongoing work to accommodate fine-grained analysis of interactive systems via model checking. We argue that this can be achieved by combining a basic abstract model of user behaviour and a separate constraint on the acceptable degree of cognitive mismatch. To explain the problem and illustrate our approach, we present a simple scenario related to number entry in infusion pumps

Reasoning about order errors in interaction

Reliability of an interactive system depends on users as well as the device implementation. User errors can result in catastrophic system failure. However, work from the field of cognitive science shows that systems can be designed so as to completely eliminate whole classes of user errors. This means that user errors should also fall within the remit of verification methods. In this paper we demonstrate how the HOL theorem prover [7] can be used to detect and prove the absence of the family of errors known as order errors. This is done by taking account of the goals and knowledge of users. We provide an explicit generic user model which embodies theory from the cognitive sciences about the way people are known to act. The user model describes action based on user communication goals. These are goals that a user adopts based on their knowledge of the task they must perform to achieve their goals. We use a simple example of a vending machine to demonstrate the approach. We prove that a user does achieve their goal for a particular design of machine. In doing so we demonstrate that communication goal based errors cannot occur

Understanding requirements engineering process: a challenge for practice and education

Reviews of the state of the professional practice in Requirements Engineering (RE) stress that the RE process is both complex and hard to describe, and suggest there is a significant difference between competent and "approved" practice. "Approved" practice is reflected by (in all likelihood, in fact, has its genesis in) RE education, so that the knowledge and skills taught to students do not match the knowledge and skills required and applied by competent practitioners. A new understanding of the RE process has emerged from our recent study. RE is revealed as inherently creative, involving cycles of building and major reconstruction of the models developed, significantly different from the systematic and smoothly incremental process generally described in the literature. The process is better characterised as highly creative, opportunistic and insight driven. This mismatch between approved and actual practice provides a challenge to RE education - RE requires insight and creativity as well as technical knowledge. Traditional learning models applied to RE focus, however, on notation and prescribed processes acquired through repetition. We argue that traditional learning models fail to support the learning required for RE and propose both a new model based on cognitive flexibility and a framework for RE education to support this model

Towards an Intelligent Tutor for Mathematical Proofs

Computer-supported learning is an increasingly important form of study since it allows for independent learning and individualized instruction. In this paper, we discuss a novel approach to developing an intelligent tutoring system for teaching textbook-style mathematical proofs. We characterize the particularities of the domain and discuss common ITS design models. Our approach is motivated by phenomena found in a corpus of tutorial dialogs that were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor for textbook-style mathematical proofs can be built on top of an adapted assertion-level proof assistant by reusing representations and proof search strategies originally developed for automated and interactive theorem proving. The resulting prototype was successfully evaluated on a corpus of tutorial dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453

Philosophy of Computer Science: An Introductory Course

There are many branches of philosophy called “the philosophy of X,” where X = disciplines ranging from history to physics. The philosophy of artificial intelligence has a long history, and there are many courses and texts with that title. Surprisingly, the philosophy of computer science is not nearly as well-developed. This article proposes topics that might constitute the philosophy of computer science and describes a course covering those topics, along with suggested readings and assignments

Cognitive Processing of Verbal Quantifiers in the Context of Affirmative and Negative Sentences: a Croatian Study

Studies from English and German have found differences in the processing of affirmative and negative sentences. However, little attention has been given to quantifiers that form negations. A picture-sentence verification task was used to investigate the processing of different types of quantifiers in Croatian: universal quantifiers in affirmative sentences (e.g. all), non-universal quantifiers in compositional negations (e.g. not all), null quantifiers in negative concord (e.g. none) and relative disproportionate quantifiers in both affirmative and negative sentences (e.g. some). The results showed that non-universal and null quantifiers, as well as negations were processed significantly slower compared to affirmative sentences, which is in line with previous findings supporting the two-step model. The results also confirmed that more complex tasks require a longer reaction time. A significant difference in the processing of same-polarity sentences with first-order quantifiers was observed: sentences with null quantifiers were processed faster and more accurately than sentences with disproportional and non-universal quantifiers. A difference in reaction time was also found in affirmatives with different quantifiers: sentences with universal quantifiers were processed significantly faster and more accurately compared to sentences with relative disproportionate quantifiers. These findings indicate that the processing of quantifiers follows after the processing of affirmative information. In the context of the two-step model, the processing of quantifiers occurs in the second step, along with negations