A Polyvariant Binding-Time Analysis for Off-line Partial Deduction

We study the notion of binding-time analysis for logic programs. We formalise the unfolding aspect of an on-line partial deduction system as a Prolog program. Using abstract interpretation, we collect information about the run-time behaviour of the program. We use this information to make the control decisions about the unfolding at analysis time and to turn the on-line system into an off-line system. We report on some initial experiments.Comment: 19 pages (including appendix) Paper (without appendix) appeared in Programming Languages and Systems, Proceedings of the European Symposium on Programming (ESOP'98), Part of ETAPS'98 (Chris Hankin, eds.), LNCS, vol. 1381, 1998, pp. 27-4

An evaluation of electronic individual peer assessment in an introductory programming course

[Abstract]: Peer learning is a powerful pedagogical practice delivering improved outcomes over conventional teacher-student interactions while offering marking relief to instructors. Peer review enables learning by requiring students to evaluate the work of others. PRAISE is an on-line peer-review system that facilitates anonymous review and delivers prompt feedback from multiple sources. This study is an evaluation of the use of PRAISE in an introductory programming course. Use of the system is examined and attitudes of novice programmers towards the use of peer review are compared to those of students from other disciplines, raising a number of interesting issues. Recommendations are made to introductory programming instructors who may be considering peer review in assignments

Robust Control of Uncertain Markov Decision Processes with Temporal Logic Specifications

We present a method for designing robust controllers for dynamical systems with linear temporal logic specifications. We abstract the original system by a finite Markov Decision Process (MDP) that has transition probabilities in a specified uncertainty set. A robust control policy for the MDP is generated that maximizes the worst-case probability of satisfying the specification over all transition probabilities in the uncertainty set. To do this, we use a procedure from probabilistic model checking to combine the system model with an automaton representing the specification. This new MDP is then transformed into an equivalent form that satisfies assumptions for stochastic shortest path dynamic programming. A robust version of dynamic programming allows us to solve for a $\epsilon$-suboptimal robust control policy with time complexity $O(\log 1/\epsilon)$ times that for the non-robust case. We then implement this control policy on the original dynamical system

Ape: An Expert System for Automatic Programming from Abstract Specifications of Data Types and Algorithms

The APE (Automatic Programming Expert) system constructs executable and efficient programs from algebraic specifications of abstract data types, and abstract algorithms given as conditional term-rewrite-rule-systems with terms built up from operation symbols of the abstract data types involved. The APE is an experimental system devised to develop methods for codifying a rather Broad extent of programming knowledge required to construct implementations of data types and algorithms. For data type specifications, the APE admits hidden operations, conditional axioms, and parameterized data types. The APE automatically implements algebraic specifications of all commonly known data types in terms of clusters of INTERLISP-functions. The APE constructs executable implementations of a variety of sorting and searching algorithms. As an experimental prototype, the APE demonstrates that a knowledge-based programming paradigm provides a useful tool for partially automating an important phase of software development