Goal Recognition through Goal Graph Analysis

We present a novel approach to goal recognition based on a two-stage paradigm of graph construction and analysis. First, a graph structure called a Goal Graph is constructed to represent the observed actions, the state of the world, and the achieved goals as well as various connections between these nodes at consecutive time steps. Then, the Goal Graph is analysed at each time step to recognise those partially or fully achieved goals that are consistent with the actions observed so far. The Goal Graph analysis also reveals valid plans for the recognised goals or part of these goals. Our approach to goal recognition does not need a plan library. It does not suffer from the problems in the acquisition and hand-coding of large plan libraries, neither does it have the problems in searching the plan space of exponential size. We describe two algorithms for Goal Graph construction and analysis in this paradigm. These algorithms are both provably sound, polynomial-time, and polynomial-space. The number of goals recognised by our algorithms is usually very small after a sequence of observed actions has been processed. Thus the sequence of observed actions is well explained by the recognised goals with little ambiguity. We have evaluated these algorithms in the UNIX domain, in which excellent performance has been achieved in terms of accuracy, efficiency, and scalability.

Resistance training enhances the stability of sensorimotor coordination

Strategics for the control of human movement are constrained by the neuroanatomical characteristics of the motor system. In particular, there is evidence that the capacity of muscles for producing force has a strong influence on the stability of coordination in certain movement tasks. In the present experiment, our aim was to determine whether physiological adaptations that cause relatively long-lasting changes in the ability of muscles to produce force can influence the stability of coordination in a systematic manner. We assessed the effects of resistance training on the performance of a difficult coordination task that required participants to synchronize or syncopate movements of their index finger with an auditory metronome. Our results revealed that training that increased isometric finger strength also enhanced the stability of movement coordination. These changes were accompanied by alterations in muscle recruitment patterns. In Particular, the trained muscles were recruited in a more consistent fashion following the programme of resistance training. These results indicate that resistance training produces functional adaptations of the neuroanatomical constraints that underlie the control of voluntary movement.

Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems

In this paper, we present a random iterative graph based hyper-heuristic to produce a collection of heuristic sequences to construct solutions of different quality. These heuristic sequences can be seen as dynamic hybridisations of different graph colouring heuristics that construct solutions step by step. Based on these sequences, we statistically analyse the way in which graph colouring heuristics are automatically hybridised. This, to our knowledge, represents a new direction in hyper-heuristic research. It is observed that spending the search effort on hybridising Largest Weighted Degree with Saturation Degree at the early stage of solution construction tends to generate high quality solutions. Based on these observations, an iterative hybrid approach is developed to adaptively hybridise these two graph colouring heuristics at different stages of solution construction. The overall aim here is to automate the heuristic design process, which draws upon an emerging research theme on developing computer methods to design and adapt heuristics automatically. Experimental results on benchmark exam timetabling and graph colouring problems demonstrate the effectiveness and generality of this adaptive hybrid approach compared with previous methods on automatically generating and adapting heuristics. Indeed, we also show that the approach is competitive with the state of the art human produced methods.

Artificial gravity reveals that economy of action determines the stability of sensorimotor coordination.

Background
When we move along in time with a piece of music, we synchronise the downward phase of our gesture with the beat. While it is easy to demonstrate this tendency, there is considerable debate as to its neural origins. It may have a structural basis, whereby the gravitational field acts as an orientation reference that biases the formulation of motor commands. Alternatively, it may be functional, and related to the economy with which motion assisted by gravity can be generated by the motor system.
Methodology/Principal Findings
We used a robotic system to generate a mathematical model of the gravitational forces acting upon the hand, and then to reverse the effect of gravity, and invert the weight of the limb. In these circumstances, patterns of coordination in which the upward phase of rhythmic hand movements coincided with the beat of a metronome were more stable than those in which downward movements were made on the beat. When a normal gravitational force was present, movements made down-on-the-beat were more stable than those made up-on-the-beat.
Conclusions/Significance
The ubiquitous tendency to make a downward movement on a musical beat arises not from the perception of gravity, but as a result of the economy of action that derives from its exploitation.

Compact Toffoli gate using weighted graph states

We introduce three compact graph states that can be used to perform a measurement-based Toffoli gate. Given a weighted graph of six, seven, or eight qubits, we show that success probabilities of 1/4, 1/2, and 1, respectively, can be achieved. Our study puts a measurement-based version of this important quantum logic gate within the reach of current experiments. As the graphs are setup independent, they could be realized in a variety of systems, including linear optics and ion traps.

Structural information content of networks: Graph entropy based on local vertex functionals

In this paper we define the structural information content of graphs as their corresponding graph entropy. This definition is based on local vertex functionals obtained by calculating-spheres via the algorithm of Dijkstra. We prove that the graph entropy and, hence, the local vertex functionals can be computed with polynomial time complexity enabling the application of our measure for large graphs. In this paper we present numerical results for the graph entropy of chemical graphs and discuss resulting properties. (C) 2007 Elsevier Ltd. All rights reserved.

Low frequency rTMS effects on sensorimotor synchronization

Previous studies using low frequency (1 Hz) rTMS over the motor and premotor cortex have examined repetitive movements, but focused either on motor aspects of performance such as movement speed, or on variability of the produced intervals. A novel question is whether TMS affects the synchronization of repetitive movements with an external cue (sensorimotor synchronization). In the present study participants synchronized finger taps with the tones of an auditory metronome. The aim of the study was to examine whether motor and premotor cortical inhibition induced by rTMS affects timing aspects of synchronization performance such as the coupling between the tap and the tone and error correction after a metronome perturbation. Metronome sequences included perturbations corresponding to a change in the duration of a single interval (phase shifts) that were either small and below the threshold for conscious perception (10 ms) or large and perceivable (50 ms). Both premotor and motor cortex stimulation induced inhibition, as reflected in a lengthening of the silent period. Neither motor nor premotor cortex rTMS altered error correction after a phase shift. However, motor cortex stimulation made participants tap closer to the tone, yielding a decrease in tap-tone asynchrony. This provides the first neurophysiological demonstration of a dissociation between error correction and tap-tone asynchrony in sensorimotor synchronization. We discuss the results in terms of current theories of timing and error correction.