Some applications of logic to feasibility in higher types

While it is commonly accepted that computability on a Turing machine in polynomial time represents a correct formalization of the notion of a feasibly computable function, there is no similar agreement on how to extend this notion on functionals, that is, what functionals should be considered feasible. One possible paradigm was introduced by Mehlhorn, who extended Cobham's definition of feasible functions to type 2 functionals. Subsequently, this class of functionals (with inessential changes of the definition) was studied by Townsend who calls this class POLY, and by Kapron and Cook who call the same class basic feasible functionals. Kapron and Cook gave an oracle Turing machine model characterisation of this class. In this article, we demonstrate that the class of basic feasible functionals has recursion theoretic properties which naturally generalise the corresponding properties of the class of feasible functions, thus giving further evidence that the notion of feasibility of functionals mentioned above is correctly chosen. We also improve the Kapron and Cook result on machine representation.Our proofs are based on essential applications of logic. We introduce a weak fragment of second order arithmetic with second order variables ranging over functions from NN which suitably characterises basic feasible functionals, and show that it is a useful tool for investigating the properties of basic feasible functionals. In particular, we provide an example how one can extract feasible programs from mathematical proofs that use nonfeasible functions.

Web searching and Σ2 queries

In the terminology of Logic programming, current search engines answer Sigma1 queries (formulas of the form where is a boolean combination of attributes). Such a query is determined by a particular sequence of keywords input by a user. In order to give more control to users, search engines will have to tackle more expressive queries, namely, Sigma2 queries (formulas of the form ). The purpose of the talk is to examine which directions could be explored in order to move towards more expressive languages, more powerful search engines, and the benefits that users should expect.

Mind change complexity of learning logic programs

The present paper motivates the study of mind change complexity for learning minimal models of length-bounded logic programs. It establishes ordinal mind change complexity bounds for learnability of these classes both from positive facts and from positive and negative facts. Building on Angluin’s notion of finite thickness and Wright’s work on finite elasticity, Shinohara defined the property of bounded finite thickness to give a sufficient condition for learnability of indexed families of computable languages from positive data. This paper shows that an effective version of Shinohara’s notion of bounded finite thickness gives sufficient conditions for learnability with ordinal mind change bound, both in the context of learnability from positive data and for learnability from complete (both positive and negative) data. Let Omega be a notation for the first limit ordinal. Then, it is shown that if a language defining framework yields a uniformly decidable family of languages and has effective bounded finite thickness, then for each natural number m >0, the class of languages defined by formal systems of length <= m: • is identifiable in the limit from positive data with a mind change bound of Omega (power)m; • is identifiable in the limit from both positive and negative data with an ordinal mind change bound of Omega × m. The above sufficient conditions are employed to give an ordinal mind change bound for learnability of minimal models of various classes of length-bounded Prolog programs, including Shapiro’s linear programs, Arimura and Shinohara’s depth-bounded linearly covering programs, and Krishna Rao’s depth-bounded linearly moded programs. It is also noted that the bound for learning from positive data is tight for the example classes considered.

Robot building for preschoolers

This paper describes Electronic Blocks, a new robot construction element designed to allow children as young as age three to build and program robotic structures. The Electronic Blocks encapsulate input, output and logic concepts in tangible elements that young children can use to create a wide variety of physical agents. The children are able to determine the behavior of these agents by the choice of blocks and the manner in which they are connected. The Electronic Blocks allow children without any knowledge of mechanical design or computer programming to create and control physically embodied robots. They facilitate the development of technological capability by enabling children to design, construct, explore and evaluate dynamic robotics systems. A study of four and five year-old children using the Electronic Blocks has demonstrated that the interface is well suited to young children. The complexity of the implementation is hidden from the children, leaving the children free to autonomously explore the functionality of the blocks. As a consequence, children are free to move their focus beyond the technology. Instead they are free to focus on the construction process, and to work on goals related to the creation of robotic behaviors and interactions. As a resource for robot building, the blocks have proved to be effective in encouraging children to create robot structures, allowing children to design and program robot behaviors.

Advanced Gen-1, 2 and 3 biofuels research in Australia - Fuelling advanced biofuels training for international scientists

Flinders University and Queensland University of Technology, biofuels research interests cover a broad range of activities. Both institutions are seeking to overcome the twin evils of "peak oil" (Hubbert 1949 & 1956) and "global warming" (IPPC 2007, Stern 2006, Alison 2010), through development of Generation 1, 2 and 3 (Gen-1, 2 & 3) biofuels (Clarke 2008, Clarke 2010). This includes development of parallel Chemical Biorefinery, value-added, co-product chemical technologies, which can underpin the commercial viability of the biofuel industry. Whilst there is a focused effort to develop Gen-2 & 3 biofuels, thus avoiding the socially unacceptable use of food based Gen-1 biofuels, it must also be recognized that as yet, no country in the world has produced sustainable Gen-2 & 3 biofuel on a commercial basis. For example, in 2008 the United States used 38 billion litres (3.5% of total fuel use) of Gen-1 biofuel; in 2009/2010 this will be 47.5 billion litres (4.5% of fuel use) and in 2018 this has been estimated to rise to 96 billion litres (9% of total US fuel use). Brazil in 2008 produced 24.5 billion litres of ethanol, representing 37.3% of the world’s ethanol use for fuel and Europe, in 2008, produced 11.7 billion litres of biofuel (primarily as biodiesel). Compare this to Australia’s miserly biofuel production in 2008/2009 of 180 million litres of ethanol and 75 million litres of biodiesel, which is 0.4% of our fuel consumption! (Clarke, Graiver and Habibie 2010) To assist in the development of better biofuels technologies in the Asian developing regions the Australian Government recently awarded the Materials & BioEnergy Group from Flinders University, in partnership with the Queensland University of Technology, an Australian Leadership Award (ALA) Biofuel Fellowship program to train scientists from Indonesia and India about all facets of advanced biofuel technology.

Coordinated road-junction traffic control by dynamic programming

This paper presents a novel approach to road-traffic control for interconnected junctions. With a local fuzzy-logic controller (FLC) installed at each junction, a dynamic-programming (DP) technique is proposed to derive the green time for each phase in a traffic-light cycle. Coordination parameters from the adjacent junctions are also taken into consideration so that organized control is extended beyond a single junction. Instead of pursuing the absolute optimization of traffic delay, this study examines a practical approach to enable the simple implementation of coordination among junctions, while attempting to reduce delays, if possible. The simulation results show that the delay per vehicle can be substantially reduced, particularly when the traffic demand reaches the junction capacity. The implementation of this controller does not require complicated or demanding hardware, and such simplicity makes it a useful tool for offline studies or realtime control purposes.

Fuzzy logic power system stabilizer in multimachine stability studies

Power system stabilizers (PSS) work well at the particular network configuration and steady state conditions for which they were designed. Once conditions change, their performance degrades. This can be overcome by an intelligent nonlinear PSS based on fuzzy logic. Such a fuzzy logic power system stabilizer (FLPSS) is developed, using speed and power deviation as inputs, and provides an auxiliary signal for the excitation system of a synchronous motor in a multimachine power system environment. The FLPSS's effect on the system damping is then compared with a conventional power system stabilizer's (CPSS) effect on the system. The results demonstrate an improved system performance with the FLPSS and also that the FLPSS is robust