By Hiroshi Motoda (auth.), Abdul Sattar, Byeong-ho Kang (eds.)
The Australian Joint convention on man made Intelligence sequence is suggested via the Australian computing device Society's (ACS) nationwide Committee on synthetic Intelligence and specialist structures. It goals at stimulating examine by way of selling trade and cross-fertilization of principles between assorted branches of synthetic intelligence. It additionally offers a typical discussion board for researchers and practitioners in quite a few fields of AI to replace new rules and percentage their event. This quantity includes the lawsuits of the nineteenth Australian Joint convention on synthetic Intelligence (AI 2006) held at Hobart, Australia. AI 2006 obtained a list variety of submissions, a complete of 689 submissions from 35 nations. From those, complete papers 89 (13%), lengthy papers (up to twelve pages) and 70 (10%) brief papers (up to 7 pages) have been authorized for presentation and integrated during this quantity. All complete papers have been reviewed via rounds of checks through not less than autonomous - audience together with Senior application Committee individuals. The papers during this quantity provide a sign of modern advances in synthetic int- ligence. the subjects lined contain laptop studying, Robotics, AI functions, making plans, brokers, facts Mining and data Discovery, Cognition and consumer Interface, imaginative and prescient and snapshot Processing, info Retrieval and seek, AI within the net, wisdom illustration, Knowledge-Based platforms, and Neural Networks.
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Additional info for AI 2006: Advances in Artificial Intelligence: 19th Australian Joint Conference on Artificial Intelligence, Hobart, Australia, December 4-8, 2006. Proceedings
Patches of highest activity for labeled letter/phoneme combinations after self-organization on a map of 36×36 neurons. 5 Robustness of the Bimodal Percepts Against Unimodal Disturbances An important advantage of integration of stimuli from sensory-speciﬁc cortices into multimodal percepts in multimodal association cortices is that even large disturbances in the stimuli may be eliminated in the multimodal percepts. Our model has the same advantage, as can easily be demonstrated. We choose to study the processing of the three letters i, ˚ a and m which are all uncorrupted.
Notice the diﬀerence in changes of activities in the phoneme map and in the bimodal map. 6 Introduction of Feedback and Its Significance for Auditory Perception The robustness of the bimodal percepts, demonstrated in Figure 5, can be employed to beneﬁt through feedback to enhance auditory perception, as is the case in cortex [15,22]. We introduce feedback in our MuSON through the re-coded phoneme map SOMrph , see the right part of Figure 1. The 6-dimensional input stimuli to the re-coded phoneme map [yph ybm ] is formed from the feedforward connection from the sensory phoneme map SOMph and the top-down feedback connection from the bimodal map SOMbm .
The patch of neurons representing ¨ o is clearly distinguished from other patches. The position of the winner can be determined from the network (map) postsynaptic activity, d(k) = W · x(k). As an example, in Figure 2, we show the post-synaptic activity of a trained bimodal map when the visual letter stimulus o xlt (k) and the auditory phoneme stimulus xph (k) representing letter/phoneme ¨ is presented. The activity in the map for one phoneme/letter combination shows one winning patch with activity descending away from this patch as illustrated in Figure 2.
AI 2006: Advances in Artificial Intelligence: 19th Australian Joint Conference on Artificial Intelligence, Hobart, Australia, December 4-8, 2006. Proceedings by Hiroshi Motoda (auth.), Abdul Sattar, Byeong-ho Kang (eds.)