Monday, 31 January 2011

Monday 31/01/2011

Firstly, this is an updated version of my Gantt chart after taking some comments on board:















Secondly, I think I have completely fixed the login and logout authentication. Different users can see their own data after logging in. It has been successfully tested. Before achieved this state, the system has driven me mad. For example, the "Home" link was broken and it just made everything totally messed.

Finally, I am ready to move on to the next task, which is to fix "Zero-gap" problem described before.

Monday, 24 January 2011

Monday 24/01/2011

Having attended to a training class today, I decided to revise my 9 months report planning. Therefore I am able to work and try to meeting deadlines.
I do know what I am going to do in short term, however it is still very vague for a long term (from Task 3). It is very hard to imagine what exactly you are going to do after 3-4 months. So need to revise it concisely again after completing Task 2.

Here is the my updated Gantt chart, any suggestion is welcome and seriously be taken into account :)

Sunday, 23 January 2011

Friday 21/01/2010

Having looked through and read the Figure Energy wiki, I have understood deeply about the system. The local database has been synchronized with the local machine now, I am able to create a event locally and play around with the system. 

I have fixed the root path link, done the login and logout page. Will need some more testing from different users before go further to the zero gap issue.

Thusday 20/01/2011

FigureEnergy Project Overview:
FigureEnergy is a project which helps to manage the electricity consumption of the consumers at home or at work.
FigureEnergy contains four main widgets: activity logger, planner, analyser and live energy activity, designed to help users to have a better understanding of energy consumption over various timescales and learn to efficiently manage the energy devices.
The activity logger is considered to be a core of the FigureEnergy platform. It relies on a Django/Python backend to pulls data from AlertMe server in the specific periods. In addition, the AlertMe system collects data from consumers’ hubs frequently (20 mins). Given this, the logger can display the energy consumption usages of the users and allows them to interact with the specific activities.
The current technical issue of the activity logger is the ‘zero gap’ , where there is no data existing from the current time to the previous collection point of AlertMe system. For example, the last collection time of AlertMe is at 3.00pm and the next collection time is at 3.50pm. We want to show the data from 2.00pm to 3.20pm, the system would show the zero gap between 3.00pm to 3.20pm as no data found on the AlertMe server yet.
Next current technical issue with Django/Python is the authentication page.  Each user would see its own data after logging into the system.

Monday, 17 January 2011

Monday 17/01/2011

Still struggling in able to execute the Figure Energy Django in Eclipse. I have tried to install Ubuntu in VMWare but still have not found the working image files yet.

Plan for today is try to run the Figure Energy in Eclipse successfully (will try two ways: i) fixing libraries in Win 7, ii)  install everything again in Ubuntu VMWare) then finish the authentication task.

Fingers Crossed!

Update:
- Finish Ubuntu  installation using VMware WorkStation.

Friday, 14 January 2011

Friday 14/01/2011

Targets to be achieved from now to next Thusday:
- Get more understand about Figure Energy stuff such as code and documentation.
- Complete the front-end login authentication of the Figure Energy Website, which can be found at: http://hci.ecs.soton.ac.uk/FigureEnergy

Plan for today:
- install Figure Energy svn to Eclipse.
- Play around with the code to understand the framework.

Tuesday, 2 November 2010

Electric Elves: Immersing an agent organisation in a human organisation (AAAI2000)

This paper firstly introduces the term of agentization in the organization. The idea is using software agents (agentization) to support for the large-scale human organizations. For example, dynamic teaming of such heterogeneous agents will enable organizations to act coherently, to robustly attain their mission goals, to react swiftly to crises, and to dynamically adapt to events.

Advances could potentially assist all organizations, including the military, civilian disaster response organizations, corporations, and universities and research institutions.
Agents here act as proxies for each person within an organisation, presented on behalf of the people or resources they represent.

Applying agent technology in human organization provides these following challenging:
·         A key research question of adjustable autonomy. It means agents acting as proxies for people must automatically adjust their own autonomy such as avoiding critical errors or possibly by letting people make important decisions.
·         The agent system must be up and running 24/7 as human organization operates continually over time.
·         People have a wide and rich variety of capabilities, interests and preferences, etc, as well as their associated tasks. Therefore agents acting as proxies must represent and reason with such capabilities and interests. It is difficult to arrange a powerful matchmaking capability to match people with similar interests.
·         Human organizations are often huge. This mean we need to scale-up in the number of agents compared against typical multiagent systems in current operation.

Electric Elves project investigates the above research issues and the impact of agentization on human organization in general, using their own Intelligent System Division of USC/ISI as a testbed. The working prototypes is about 10 agent proxies running continuously and automatically manages some tasks such as selecting teams for giving a demonstration, scheduling and rescheduling meetings, monitoring the location of users.

The project used Teamcore, a domain-independent, decentralised, teamwork-based integration architecture to construct the organizations of software agents. The Teamcore proxy considers only the team-level activities. It additionally used the Friday agent to serve the user’s interests in dealing with Teamcore proxy. The Friday agent is to ensure proper consideration of each person’s individual preferences.  The Friday uses C4.5 to learn a decision tree from user’s feedbacks in training mode. Then Friday applies its learned knowledge in circumstances to act autonomously in actual use mode.
The Teamcore used the capabilities matcher and the interest matcher combined with the statistical information retrieval (IR) techniques with knowledge-based matchmaking based on logical inference to form the team.
The Electric Elves project has ran the agent proxies to coordinate real meeting schedules for a group of five agentized people. They found it smoothly and helpful, except the limitation of using C4.5 for agent’s learning paths.

This paper has been written in 2000, presented a vision for a complex, heterogeneous agent organization that automates numerous tasks central to human organizations. The Teamcore’s infrastructural teamwork could potentially facilitate such agents to work together in teams. The paper has not yet attempted to apply the agentization of the large-scale human organisations. They said they would do it but I doubt about that.