2175115
Phase Transitions in Self-Organising Sensor Networks
In this paper we consider a multi-cellular sensing and communication network, embedded in an ageless aerospace vehicle, that is expected to detect and react to impact location, intensity and damage over a wide range of impact energies. In particular, we investigate self-organization of impact boundaries enclosing critically damaged areas, and measure their spatiotemporal robustness. The presented quantitative information-theoretic techniques clearly identify phase transitions, separating chaotic dynamics from ordered and robust patterns.
44750839
Relating the Entropy of Joint Beliefs to Multi-agent Coordination
Current approaches to activity coordination in multi-agent systems (teams) range from strictly top down (plan-based coordination) to purely emergent (reactive coordination), with many hybrid variants, each having its specific advantages and disadvantages. It appears to be extremely difficult to rigorously compare various hybrid approaches to multi-agent coordination (and communication), given the lack of a generic semantics or some guidelines. In this paper, we studied some intuitive inter-agent communication policies and characterised them in terms of generic information-theoretic properties. In particular, the relative entropy of joint beliefs was suggested as an indicator of teams coordination potential. Our novel behaviour-based agent architecture (based on the Deep Behaviour Projection framework) enabled consistent reasoning about belief change, including beliefs about other agents. This allowed us to examine some of the identified communication policies empirically. The obtained results confirmed that there are certain interesting invariants – in particular, a change in team coordination (and overall performance) was shown to be within the boundaries indicated by the relative information entropy.
8410122
On connectivity of reconfigurable impact networks in ageless aerospace vehicles
The research results presented in this paper were obtained as part of the joint CSIRO-NASA Ageless Aerospace Vehicle (AAV) project. We describe the underlying principles, methodology, and preliminary results of modelling and simulating a multi-cellular sensor and communication network in a dynamic decentralised setting, motivated by a self-monitoring, self-repairing AAV. Such networks are expected to detect and react to multiple impacts and damage over a wide range of impact energies. In particular, we address the problem of forming a reconfigurable network (a minimum spanning tree) connecting cells that detected non-critical impacts, in presence of connectivity disruptions caused by critical impacts. The presented algorithm is based on the ant colony metaphor and may be complemented by gradient-based techniques. In addition, we measure the robustness of impact networks and present quantitative metrics that clearly identify phase transitions in network connectivity, separating chaotic dynamics from ordered and robust patterns. © 2005 Elsevier B.V. All rights reserved.
15572586
Complexity metrics for self-monitoring impact sensing networks
In this paper we describe novel metrics measuring complexity in self-organising networks. The metrics are investigated within the context of decentralised inspections, developed and implemented as part of the joint CSIRO-NASA Ageless Aerospace Vehicle (AAV) research project. The AAV Concept Demonstrator is a hardware multi-cellular sensing and communication network which is expected to detect and react to multiple impacts, without any centralised controllers. We present an extension of an ant colony optimisation algorithm, using an adaptive dead reckoning scheme and producing robust and reconfigurable minimum spanning trees connecting autonomous AAV cells. We then introduce a new metric detecting emergence through irregularities in the multi-agent communications, and contrast it with conventional macro-level ("global-view") graph-theoretic metrics.
15025660
On Convergence of Dynamic Cluster Formation in Multi-agent Networks
Efficient hierarchical architectures for reconfigurable and adaptive multi-agent networks require dynamic cluster formation among the set of nodes (agents). In the absence of centralised controllers, this process can be described as self-organisation of dynamic hierarchies, with multiple cluster-heads emerging as a result of inter-agent communications. Decentralised clustering algorithms deployed in multi-agent networks are hard to evaluate precisely for the reason of the diminished predictability brought about by self-organisation. In particular, it is hard to predict when the cluster formation will converge to a stable configuration. This paper proposes and experimentally evaluates a predictor for the convergence time of cluster formation, based on a regularity of the inter-agent communication space as the underlying parameter. The results indicate that the generalised “correlation entropy” K2 (a lower bound of Kolmogorov-Sinai entropy) of the volume of the inter-agent communications can be correlated with the time of cluster formation, and can be used as its predictor.
16423538
On Decentralised Clustering in self-monitoring networks
A Decentralised Adaptive Clustering (DAC) algorithm for multiagent networks is contrasted with a Fixed-order Centralised Adaptive Clustering algorithm (FCAC). The clustering is done on sensor readings detected within a self-monitoring impact sensing network. Simulation results show that DAC algorithm scales well with increasing network and data sizes and in some cases outperforms FCAC algorithm. While the common-sense intuition suggests that centralised algorithm is always superior, we support the simulation results with a simple counter-example.
34131767
Problem Solving With Insufficient Resources
A new approach, "controlled concurrency," is introduced for inference control in an adaptive reasoning system working with insufficient knowledge and resources. With this method, a problem-solving process is constructed from atomic steps in run time, according to the system's past experience and the current context. The system carries out many such processes in parallel by distributing its resources among them, and dynamically adjusting the distribution according to feedback. A data structure, "bag," is designed to support this dynamic time-space allocation, and is a kind of probabilistic priority queue. This approach provides a flexible, efficient, and adaptive control mechanism for real-time systems working with uncertain knowledge. To analyze problem solving in such a system, the traditional computability theory and computational complexity theory become inappropriate, because the system no longer follows problem-specific algorithms in problem solving.
38659056
Intelligent Pattern Recognition and Applications
2424601
Users' interaction with World Wide Web resources: an exploratory study using a holistic approach
Abstract This paper presents the first part of the research on user–Web interaction: a multidimensional model, methodology, and general findings. The objectives of this study are three-fold: (1) to explore factors of user–Web interaction in finding factual information and what happens during this interaction; (2) to develop a conceptual framework for studying user–Web interaction; and (3) to apply a process-tracing method for conducting holistic user–Web studies. The proposed model consists of three components: user, interface, and the World Wide Web. User–Web interaction is viewed as a communication process facilitated through an interface. A process-tracing technique has been designed to capture the processes of user-Web interactions. Twenty-four graduate students participated in this study. Prior to the interaction, each participant was given a questionnaire to report his/her computer and Web experience, the State Trait Anxiety Inventory (form Y1 and form Y2) to measure affective states, and an individually administered Embedded Figure Test to measure cognitive style. Each participant used the Web to find answers to two factual questions. Both the processes (continuous screen shots) and the concurrent verbalizations of thoughts were recorded in synchronized video–audio data. The findings provided rich information on users’ cognitive, affective and physical behaviors. The proposed model is used to present the findings of user behavior in connections with interfaces and the Web.
25531643
Multicomponent chirp signals analysis using product cubic phase function
This paper presents an algorithm for estimating the parameters of multicomponent chirp signals. The estimator is based on the cubic phase function (CPF), which is efficient to estimate the parameters of monocomponent first-, second-, and third-order polynomial phase signal. When the CPF is dealing with multicomponent chirp signals, the spurious peaks arise and hence the identifiability problem exists. A new approach based on the product cubic phase function (PCPF) is proposed to remove the identifiability problem. The occurrence probability of spurious peak, and the effect of noise to the estimator are statistically studied. The simulation examples are provided for validating the theoretical analysis.
198169122
An adaptive Modular Neural Network with Application to unconstrained Character Recognition