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.

Output feedback control of discrete-time systems in networked environments

This correspondence paper addresses the problem of output feedback stabilization of control systems in networked environments with quality-of-service (QoS) constraints. The problem is investigated in discrete-time state space using Lyapunov’s stability theory and the linear inequality matrix technique. A new discrete-time modeling approach is developed to describe a networked control system (NCS) with parameter uncertainties and nonideal network QoS. It integrates a network-induced delay, packet dropout, and other network behaviors into a unified framework. With this modeling, an improved stability condition, which is dependent on the lower and upper bounds of the equivalent network-induced delay, is established for the NCS with norm-bounded parameter uncertainties. It is further extended for the output feedback stabilization of the NCS with nonideal QoS. Numerical examples are given to demonstrate the main results of the theoretical development.

Where money and meanings meet : theorizing the emergence of new values in media and education

Recent developments in technology, globalization, and consumer activism have challenged the "broadcasting model" of natonally bounded, vertically integrated, monopolistic, expert-paradigm media industries, dedicated to supplying leisure entertainment to more or less passive consumers. Instead, attention has turned to globally traded formats, social network markets, consumer-created content, multiplatform "publication," and a semiotic long tail where niche representations can be as valuable as blockbusters. Such chenges are just as much a challenge to education as they are to business models. And education, both formal and informal, is a dynamic agent in these processes, participation, and creative content require a rethink of "studies" just as much as of "media."

Risky business : making decisions off the cuff

In line with accepted decision making theory, individuals engage in rational decision making. Decisions made under conditions of bounded rationality may have serious adverse consequences. Employees making decisions on behalf of their employer are often faced with situations where perfect and complete information is not available, and time is limited. Under such conditions, we posit that employees will make decisions that are increasingly bounded. At its most extreme neither time nor information is available to make a decision and rational decision making, bounded or not, reaches its limits. Many authors suggest that this is the point at which improvisation takes place. Although opinion in the literature is mixed regarding the efficacy of improvised decisions, we argue that improvised decisions place the organisation at considerable risk and as a consequence are undesirable.

Social infrastructure planning and sustainable community : example from south east Queensland, Australia

Social infrastructure and sustainable development represent two distinct but interlinked concepts bounded by a geographic location. For those involved in the planning of a residential development, the notion of social infrastructure is crucial to the building of a healthy community and sustainable environment. This is because social infrastructure is provided in response to the basic needs of communities and to enhance the quality of life, equity, stability and social well being. It also acts as the building block to the enhancement of human and social capital. While acknowledging the different levels of social infrastructure provision from neighbourhood, local, district and sub-regional levels, past evidence has shown that the provision at neighbourhood and local level and are affecting well-being of residents and the community sustainability. With intense physical development taking place in Australia's South East Queensland (SEQ) region, local councils are under immense pressure to provide adequate social and community facilities for their residents. This paper shows how participation-oriented, need-sensitive Integrated Social Infrastructure Planning Guideline is used to offer a solution for the efficient planning and provision of multi-level social infrastructure for the SEQ region. The paper points out to the successful implementation of the guideline for social infrastructure planning in multiple levels of spatial jurisdictions of Australia's fastest growing region.

The concealed difficulties of aligning CRE decisions to corporate goals

Realisation of the importance of real estate asset strategic decision making has inspired a burgeoning corporate real estate management (CREM) literature. Much of this criticises the poor alignment between strategic business direction and the ‘enabling’ physical environment. This is based on the understanding that corporate real estate assets represent the physical resource base that supports business, and can either complement or impede that business. In the hope of resolving this problem, CRE authors advocate a deeper integration of strategic and corporate real estate decisions. However this recommendation appears to be based on a relatively simplistic theoretical approach to organization where decision-making tends to be viewed as a rationally managed event rather than a complex process. Defining decision making as an isolated event has led to an uncritical acceptance of two basic assumptions: ubiquitous, conflict-free rationality and profit maximisation. These assumptions have encouraged prescriptive solutions that clearly lack the sophistication necessary to come to grips with the complexity of the built and organizational environment. Alternatively, approaching CREM decision making from a more sophisticated perspective, such as that of the “Carnegie School”, leads to conceptualise it as a ‘process’, creating room for bounded rationality, multiple goals, intra-organizational conflict, environmental matching, uncertainty avoidance and problem searching. It is reasonable to expect that such an approach will result in a better understanding of the organizational context, which will facilitate the creation of organizational objectives, assist with the formation of strategies, and ultimately will aid decision.

The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classification problem and the learning algorithm finds a network with small weights that has small squared error on the training patterns, then the generalization performance depends on the size of the weights rather than the number of weights. For example, consider a two-layer feedforward network of sigmoid units, in which the sum of the magnitudes of the weights associated with each unit is bounded by A and the input dimension is n. We show that the misclassification probability is no more than a certain error estimate (that is related to squared error on the training set) plus A3 √((log n)/m) (ignoring log A and log m factors), where m is the number of training patterns. This may explain the generalization performance of neural networks, particularly when the number of training examples is considerably smaller than the number of weights. It also supports heuristics (such as weight decay and early stopping) that attempt to keep the weights small during training. The proof techniques appear to be useful for the analysis of other pattern classifiers: when the input domain is a totally bounded metric space, we use the same approach to give upper bounds on misclassification probability for classifiers with decision boundaries that are far from the training examples.

Rademacher and Gaussian complexities: Risk bounds and structural results

We investigate the use of certain data-dependent estimates of the complexity of a function class, called Rademacher and Gaussian complexities. In a decision theoretic setting, we prove general risk bounds in terms of these complexities. We consider function classes that can be expressed as combinations of functions from basis classes and show how the Rademacher and Gaussian complexities of such a function class can be bounded in terms of the complexity of the basis classes. We give examples of the application of these techniques in finding data-dependent risk bounds for decision trees, neural networks and support vector machines.

On the data consumption benefits of accepting increased uncertainty

In the context of learning paradigms of identification in the limit, we address the question: why is uncertainty sometimes desirable? We use mind change bounds on the output hypotheses as a measure of uncertainty and interpret ‘desirable’ as reduction in data memorization, also defined in terms of mind change bounds. The resulting model is closely related to iterative learning with bounded mind change complexity, but the dual use of mind change bounds — for hypotheses and for data — is a key distinctive feature of our approach. We show that situations exist where the more mind changes the learner is willing to accept, the less the amount of data it needs to remember in order to converge to the correct hypothesis. We also investigate relationships between our model and learning from good examples, set-driven, monotonic and strong-monotonic learners, as well as class-comprising versus class-preserving learnability.

High-probability regret bounds for bandit online linear optimization

We present a modification of the algorithm of Dani et al. [8] for the online linear optimization problem in the bandit setting, which with high probability has regret at most O ∗ ( √ T) against an adaptive adversary. This improves on the previous algorithm [8] whose regret is bounded in expectation against an oblivious adversary. We obtain the same dependence on the dimension (n 3/2) as that exhibited by Dani et al. The results of this paper rest firmly on those of [8] and the remarkable technique of Auer et al. [2] for obtaining high probability bounds via optimistic estimates. This paper answers an open question: it eliminates the gap between the high-probability bounds obtained in the full-information vs bandit settings.

REGAL : a regularization based algorithm for reinforcement learning in weakly communicating MDPs

We provide an algorithm that achieves the optimal regret rate in an unknown weakly communicating Markov Decision Process (MDP). The algorithm proceeds in episodes where, in each episode, it picks a policy using regularization based on the span of the optimal bias vector. For an MDP with S states and A actions whose optimal bias vector has span bounded by H, we show a regret bound of ~ O(HS p AT ). We also relate the span to various diameter-like quantities associated with the MDP, demonstrating how our results improve on previous regret bounds.

The undirected feedback vertex set problem has a Poly(k) Kernel

Resolving a noted open problem, we show that the Undirected Feedback Vertex Set problem, parameterized by the size of the solution set of vertices, is in the parameterized complexity class Poly(k), that is, polynomial-time pre-processing is sufficient to reduce an initial problem instance (G, k) to a decision-equivalent simplified instance (G', k') where k' � k, and the number of vertices of G' is bounded by a polynomial function of k. Our main result shows an O(k11) kernelization bound.

The influence of cultural selection on strategic communication

Strategic communication is held to be a key process by which organisations respond to environmental uncertainty. In the received view articulated in the literatures of organisational communication and public relations, strategic communication results from collaborative efforts by organisational members to create shared understanding about environmental uncertainty and, as a result of this collective understanding, formulate appropriate communication responses. In this study, I explore how such collaborative efforts towards the development of strategic communication are derived from, and bounded by, culturally shared values and assumptions. Study of the influences of an organisation‟s culture on the formulation of strategic communication is a fundamental conceptual challenge for public relations and, to date, a largely unaddressed area of research. This thesis responds to this challenge by describing a key property of organisational culture – the action of cultural selection (Durham, 1992). I integrate this property of cultural selection to extend and refine the descriptive range of Weick‟s (1969, 1979) classic sociocultural model of organizing. From this integration I propose a new model, the Cultural Selection of Strategic Communication (CSSC). Underpinning the CSSC model is the central proposition that because of the action of cultural selection during organizing processes, the inherently conservative properties of an organisation‟s culture constrain development of effective strategic communication in ways that may be unrelated to the outcomes of “environmental scanning” and other monitoring functions heralded by the public relations literature as central to organisational adaptation. Thus, by examining the development of strategic communication, I describe a central conservative influence on the social ecology of organisations. This research also responds to Butschi and Steyn‟s (2006) call for the development of theory focusing on strategic communication as well as Grunig (2006) and Sriramesh‟s (2007) call for research to further understand the role of culture in public relations practice. In keeping with the explorative and descriptive goals of this study, I employ organisational ethnography to examine the influence of cultural selection on the development of strategic communication. In this methodological approach, I use the technique of progressive contextualisation to compare data from two related but distinct cultural settings. This approach provides a range of descriptive opportunities to permit a deeper understanding of the work of cultural selection. Findings of this study propose that culture, operating as a system of shared and socially transmitted social knowledge, acts through the property of cultural selection to influence decision making, and decrease conceptual variation within a group. The findings support the view that strategic communication, as a cultural product derived from the influence of cultural selection, is an essential feature to understand the social ecology of an organisation.

Choosing landmarks for risky planning

This work examines the effect of landmark placement on the efficiency and accuracy of risk-bounded searches over probabilistic costmaps for mobile robot path planning. In previous work, risk-bounded searches were shown to offer in excess of 70% efficiency increases over normal heuristic search methods. The technique relies on precomputing distance estimates to landmarks which are then used to produce probability distributions over exact heuristics for use in heuristic searches such as A* and D*. The location and number of these landmarks therefore influence greatly the efficiency of the search and the quality of the risk bounds. Here four new methods of selecting landmarks for risk based search are evaluated. Results are shown which demonstrate that landmark selection needs to take into account the centrality of the landmark, and that diminishing rewards are obtained from using large numbers of landmarks.

Stepwise refinement of interrupt-driven real-time programs

Embedded real-time programs rely on external interrupts to respond to events in their physical environment in a timely fashion. Formal program verification theories, such as the refinement calculus, are intended for development of sequential, block-structured code and do not allow for asynchronous control constructs such as interrupt service routines. In this article we extend the refinement calculus to support formal development of interrupt-dependent programs. To do this we: use a timed semantics, to support reasoning about the occurrence of interrupts within bounded time intervals; introduce a restricted form of concurrency, to model composition of interrupt service routines with the main program they may preempt; introduce a semantics for shared variables, to model contention for variables accessed by both interrupt service routines and the main program; and use real-time scheduling theory to discharge timing requirements on interruptible program code.