Multimodal Fusion, Fission and Virtual Reality Simulation for an Ambient Robotic Intelligence

Abstract

In this paper, we present an architecture that demonstrates multimodal fusion and fission using semantic agents and web services that interacts with worldwide-web computers, services and users. This solution extracts the meaning of a situation using the semantic memory of agents to manage the interaction process involved. The fusion of values from different sensors produces an event that needs implementation. The fission process suggests a detailed set of actions that are for implementation. Before such actions are implemented by actuators, these actions are first evaluated in a virtual environment which mimics the real-world environment. If no danger arises from such virtual evaluation, then implementation is feasible. Otherwise, there might be a need to add one or more smaller actions to render the action safe and free from danger. Our work presents the following contributions: (i) a design of agent memory and a model of the world environment using a knowledge representation language that is compatible with the current standards, (ii) creation of a pervasive architecture with several scenarios of composition and adaptation, (iii) presentation of how agents and services interact to provide support in a real-world, and (iv) simulation of an event in a virtual environment to assess the feasibility of the event's implementation.

Application of augmented reality as a means of interdisciplinary learning

“…Another project develops virtual reality on a robotic intelligent environment through a multimodal interaction architecture. The development of tasks or processes with little intervention from a human being, using semantic agents and web services to store information of objects and evaluate actions in virtual scenarios [20].…”

“…The above due to the positive or negative effect generated by the speed with which the data was processed. This can be evidence in a virtual environment [20], which focused on the development of a robotic intelligent environment. This resulted in the reduction of consistency and performance reduction when multiple events occurred.…”

Knowledge Processing Using EKRL for Robotic Applications

“…These latter fail to express knowledge involving natural language interaction because the binary structure of languages like OWLislimitedwhenitcomestodescribecomplexsituationsandrelationsbetweenobjects. (Adjali, 2015)proposedamultimodalarchitectureforanambientintelligenceapplicationthatmakesuse ofEKRLlanguage.Obviously,theultimategoalwouldbetoproviderobotsmeanstoconductfull human-languagedialoguetointeractwithhumananditsenvironment.KnowRob (Tenorth,2016) is a knowledge processing infrastructure for cognition-enabled robots that relies on the openCyc OWLontologyandPrologtoprovideautonomousrobotswiththeknowledgeandtoolsforstandard reasoning.ButitalsoprovidesprobabilisticreasoningbasedonBayesiannetworkswhichareanotable assetcomparingtoequivalentroboticplatforms.Ascanbenoted,fewroboticframeworkspropose tohandleuncertaintywhichfailtosolverealworldproblems.WethusextendtheEKRLframework withstatisticalmodelstorepresentandreasonwithuncertainknowledgeusingMarkovlogicnetworks whichiscrucialforthemanagementoftheinherentuncertaintyofreal-worldrobotenvironments.…”

Combination of Multi-Agent Systems and Wireless Sensor Networks for the Monitoring of Cattle

“…Due to the miniaturization of hardware, devices are gradually reaching out to all the levels of society [ 2 ]. This fact, combined with the lowering production costs of consumer electronics, has favored the rise of Ambient Intelligence (AmI) in different areas such as robotics [ 3 ], health [ 4 ], education [ 5 ] or the transport industry [ 6 ]. In short, we can see how the application of AmI techniques has spread to practically every field.…”