Embedding intelligence in enhanced music mapping agents

M.Sc. (Computer Science)Artificial Intelligence has been an increasing focus of study over the past years. Agent technology has emerged as being the preferred model for simulating intelligence [Jen00a]. Focus is now turning to inter-agent communication [Jen00b] and agents that can adapt to changes in their environment. Digital music has been gaining in popularity over the past few years. Devices such as Apple’s iPod have sold millions. These devices have the capability of holding thousands of songs. Managing such a device and selecting a list of songs to play from so many can be a difficult task. This dissertation expands on agent types by creating a new agent type known as the Modifiable Agent. The Modifiable Agent type defines agents which have the ability to modify their intelligence depending on what data they need to analyse. This allows an agent to, for example, change from being a goal based to a learning based agent, or allows an agent to modify the way in which it processes data. Digital music is a growing field with devices such as the Apple iPod revolutionising the industry. These devices can store large amounts of songs and as such, make it very difficult to navigate as they usually don’t include devices such as a mouse or keyboard. Therefore, creating a play list of songs can be a tiresome process which can lead to the user playing the same songs over and over. The goal of the dissertation is to provide research into methods of automatically creating a play list from a user selected song, i.e. once a user selects a song, a list of similar music is automatically generated and added to the user’s playlist. This simplifies the task of selecting music and adds diversity to the songs which the user listens to. The dissertation introduces intelligent music selection, or selecting a play list of songs depending on music classification techniques and past human interaction