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7. The Sulawesi project.

Sulawesi: An intelligent user interface system for ubquitous computing.

7.1 Background

A few years ago, wearable computers were dedicated systems constructed by and for a single person. The machine was customised to suit the owners personal preferences using alternative input/output devices to achieve different interaction techniques, and until now most of the interfaces used on these machines have been an amalgamation of existing desktop user interface systems and novel input/output devices.

The ideal human-computer interface for use in a mobile/ubiquitous environment would be one which listens for its user, understands what the user has asked it to do using speech recognition, gestures, machine vision and other channels of information, carried out the users request automatically, and presented the results back to the user when it is most appropriate and in a suitable format. For example; a machine which could monitor the users respiratory levels, heart rate and movement, the user could ask ``when I fall asleep could you turn off those <user pointing> lights''. This type of interaction with a mobile device or an ubiquitous environment, using spoken sentences and gestures, fall under the category of multi-modal and intelligent user interfaces; and Sulawesi is a framework which provides a basic multimodal development system.

7.2 The Sulawesi Architecture

The Sulawesi system that has been designed comprises of three distinct parts,

Programming API's allow third partys to create new input, service and output modules and integrate them with Sulawesi.

The input stage

The system gathers real world information through a well defined API. The current implementation includes a keyboard input, a network input, a speech recognition input, a video camera input, a G.P.S. input and infra-red input. The inputs do not do any pre-processing of the data, they only provide the raw data to the core of the system for interpretation by the services within.

The core stage

The core of the system contains a basic natural language processor which performs sentence translations. This converts a sentence into a command stream from which two pieces of information are extracted, which service to invoke and how the output should be rendered. A service manager is responsible for the instantiation and monitoring of the services, it also checkpoints commands to try and provide some kind of resiliance against system failures. The services produce, where possible, a modal neutral output which can be send to the output stage for processing.

The output stage

The output stage takes a modal neutral result from a service and makes a decision on how to render the information. The decision is made based on two criteria, what the user has asked for, and how the system percieves the users current context/environment.

If the user has asked to be shown a piece of information, this implies a visual rendition. If the system detects that the user is moving at speed (through the input sensors) an assumption can be made that the user attention might be distracted if a screen with the results in is displayed in front of them. (imagine what would happen if the user was driving!).. In this case the system will override the users request and would redirect the results to a more suitable renderer, such as speech.

7.3 Sentence translations

When humans recognise speech they do not understand every word in a sentence, sometimes words are misheard or a distraction prevents the whole sentence from being heard. A human can infer what has been said from the other words around the ones missed in a sentence, this is not always sucessfull but in most cases it is satisfactory for the understanding of a conversation. This type of sentence decoding has been called semi-natural language processing and has been implemented using a few basic rules, the example below explains how the system converts human understandable sentences into commands that the system understands :

It can be argued that in practice these sentences result in similar information being relayed to a user. The request is for the machines interpretation of the time to be sent to an appropriate output channel, the result is the user receiving the knowledge of what the time is. Closer inspection reveals that almost all the data in the sentences can be thrown away and the request can still be inferred from the resulting information. In the example above there has been a reduction to 1/4 and 2/9 of the number of words (data) in the sentences, while it can be argued that close to 100% of the information content is still intact.

The system implemented allows sentences to be processed and interpreted. The semi-natural language processing is achieved through a self generated lookup table of services and a language transformation table.

The service names have to be unique (due to the restrictions on the file system) and this provides a simple mechanism to match a service such as ``time'' within a sentence. It is impractical and almost impossible to hard code all predefined language transformations, and such a system would not be easily adaptable to diverse situations. The use of lookup tables provides a small and efficient way in which a user can customise the system to their own personal preferences without having to re-program or re-compile the sentence understanding code. The system knows what the words 'show' and 'tell' mean in the sentences by referring to the lookup table to determine which output renderer the results should be sent to.

Example of a lookup file.

|tell|speak| 
|read|speak| 
|show|text| 
|display|text| 
|EOF| 
The top entry in this lookup table specifies that the first time the word "say" is encountered in a sentence the results of the service should be sent to the "speak" output renderer.

The use of lookup tables inherently restricts the use of sentences, in order to create a sentence which is to be understood the following rule must be adhered to.

<render type> <service name> <service arguments> 

7.4 Summary

The above system enables a sentence like ``I would like you to turn the lights on when it gets dark''. The system interprets the sentence as a request to invoke the `light' service and to render the output using some kind of light controller device to turn on or off the lights. There are two points which need to be emphasised here, the first is on the machine inferring a meaning from a relatively natural sentence rather than the user having to adapt to the machine and remember complex commands or manipulate a user interface. The second is on the machine being asked to perform a certain task when certain conditions are met in the real world, ``when it gets dark'' requests that when the computers interpretation of the current lighting conditions cross a certain threshold, it should respond and send a message to the light controller output.

The Sulawesi system provides the flexability to achieve this type of interaction but it does not provide the underlying mechanisms for controlling lighting circuits, that's the part you have to code up ;)..

Online documentation and downloads can be found here:- http://wearables.essex.ac.uk/sulawesi/


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