Genkanban 1.0b4 is now available for download.

This release fixes a number of user interface bugs, including focus issues and
case-sensitive autofilling. This release also adds a variety of new observations to the iTunes observer.

Prerequisites

MacOS X 10.5 “Leopard”

Growl (Recommended): Genkanban can be used without the Growl notification system by viewing the current predictions, but this occupies precious screen real estate and you attention. In order to receive visual notifications of updated predictions, I recommend that you install and configure the Growl notification system before running Genkanban.

Source

There is not a source distribution available yet, but if you’d like the source code, it is available via Subversion at

http://context-macosx.googlecode.com/svn/trunk/Tools/Applications/Genkanban/

The source requires Xcode 3 to build.

Bugs, suggestions, or comments?

Please post any feedback in the comments below or send it to context@aetherial.net. This is early release software, so there will be bugs, and the application will only improve when problems are identified and submitted.

Genkanban ships with a variety of default sensors, but it also includes some functionality that allows you to create your own sensors using AppleScript. The process is simple and I’ll demonstrate it by creating a sensor that detects your currently selected weblog in MarsEdit.

Before we get into the details, here are some general notes about the AppleScript sensors:

1. AppleScript sensors are standard .scpt files authored in Script Editor.

2. The AppleScript files should be placed in the folder at

~/Library/Application Support/Genkanban/AppleScript/Sensors

3. On a set duration (currently 10 seconds), Genkanban scans that folder, and executes each script located within. The predictions generated by each script last for the set duration and will disappear once that duration expires or the observation is renewed on the next pass.

4. Scripts return observations by returning a semicolon-delimited string of key-value pairs in the form

key1=value1;key2=value2;...;keyN=valueN

The keys and values may contain any character, save “=” and “;”.

5. Genkanban translates the key-value pairs as separate observations in the form

AppleScript Observer (KEY) = VALUE

That said, let’s create a sensor for MarsEdit.

1. First, we need to create a script file to return the selected weblog. In Script Editor, enter the following script:

tell application "Finder"
    repeat with p in (processes whose name is "MarsEdit")
        tell application "MarsEdit"
            return "Marsedit: Current Blog=" & name of selected weblog
        end tell
    end repeat
end tell

Wrapping the call with the call to Finder allows us to only return a value when MarsEdit is actually running. If this was not included, the sensor would launch MarsEdit (if closed) each time it ran.

2. Test the script by running it with MarsEdit open and closed. When close, it should return nothing. When open, it should return something like:

"Marsedit: Current Blog=The Context Blog"

3. Save the script file and put it in the location mentioned above.

4. If Genkanban is running, in less than ten seconds, the script will be executed and the results will be visible in the observations window:

5. Congratulate yourself on a job well done.

Some parting thoughts: At the moment, Genkanban uses a ten second interval for executing and retiring observations. I may extend this interval to longer period or make it user-configurable. There’s a tradeoff between having reliable current observations and executing the scripts too often that I’m still negotiating.

Genkanban 1.0b3 is now available for download.

This is the first public release of Genkanban and should be considered quite beta. There are likely the normal memory leaks and other issues present in early release software. That said, I have been using this version for some time and it hasn’t presented me with any major problems.

This release fixes a number of UI bugs present in prior private releases.

Prerequisites

MacOS X 10.5 “Leopard”

Growl (Recommended): Genkanban can be used without the Growl notification system by viewing the current predictions, but this occupies precious screen real estate and you attention. In order to receive visual notifications of updated predictions, I recommend that you install and configure the Growl notification system before running Genkanban.

Source

There is not a source distribution available yet, but if you’d like the source code, it is available via Subversion at

http://context-macosx.googlecode.com/svn/trunk/Tools/Applications/Genkanban/

The source requires Xcode 3 to build.

Bugs, suggestions, or comments?

Please post any feedback in the comments below or send it to context@aetherial.net. This is early release software, so there will be bugs, and the application will only improve when problems are identified and submitted.

At its core Genkanban does two things: predicts your context and takes actions on the basis of the predicted context. When first installed, Genkanban does not “know” you and cannot predict your context until it is trained. There are two methods of training Genkanban, described below. In order for Genkanban to function, you must first spend some time training it.

All of the functionality described below is accessible from the Genkanban status menu represented by a bell in menu bar:

Training method 1: Uniform sampling

Using uniform sampling of your context, Genkanban collects context labels from you at regular intervals. This is the conceptual equivalent of someone asking you every five minutes, “Am I doing it right?”

To enable this training method, open the Preferences and select the Training tab. Within this tab, you can select a training interval ranging from thirty seconds to five minutes. This is how often Genkanban will interrupt you for new context labels. You may choose to enable an audible cue that sounds with the interruption. This sound is a small bell and may be useful if you work on a busy desktop where a small window my be lost among the others.

To begin training, click the “Start Training” button. The system will begin to interrupt you periodically until you stop the training. You may adjust the interval or toggle the audible cue without stopping and restarting the training process. In addition to the button within the preferences, training may be toggled using the “Start Training” menu item under the bell icon in your status bar. Restarting training does not begin the process anew – instead it simply continues from where it left off.

(Note that as of 1.0b3, there is no “reset training” functionality. You would like to start over, please post a comment below and I’ll provide instructions for resetting training. This functionality will be included in a future revision.)

Training method 2: Correct on error

If you do not want to be interrupted periodically and would like to take responsibility for monitoring Genkanban’s training, you can use a simple “correct on error” approach to teaching Genkanban. To do this, select “Predictions…” from the bell menu in your status menu. This will bring up a translucent black heads-up display (HUD) that can be placed anywhere on your screen. This HUD displays the context Genkanban currently predicts:

If you see that Genkanban is making an incorrect prediction, you can correct it by selecting “Correct Current Prediction…” from the bell status menu. This brings up the window used to train Genkanban and you can select an existing label or enter a new one. When you click the “Set” button, your current context and environment is sent to the machine learner and this generates a new prediction based on the new evidence.

The pros and cons of the different methods

The uniform sampling training method will collect more information about you as you use the computer throughout the day. You will not have to remember to give it new information, as it will periodically interrupt you for context labels.

The “correct on error” method eliminates the interruptions at the cost of you being responsible for verifying the predictions of the system. Ideally, this method only collects labels that contradicts the existing model, so the a wider variety of contexts should be predictable using a smaller number of samples.

Please note that these training methods are not mutually exclusive and can be intermixed as desired. You can correct the system’s current prediction while using the uniform sampling method. You my choose to begin using the uniform sampling method and then switch over to the “correct on error” method.

I personally prefer to use the “correct on error” method to avoid the regular interruptions. However, I do not know which method is the optimal one for building a more reliable model. This is an open research question that I plan to answer while building this system.

As I mentioned in my previous post, I am currently creating a context-sensing application for the Mac that will serve as the basis of my research into other context-aware applications. At the moment, that application is Genkanban.

According to my Japanese-English dictionary, the word genkanban is the closest Japanese equivalent to the English word concierge. Like a hotel concierge, Genkanban’s purpose is to watch and learn from you so that it can relay it’s nuanced understanding of your context to other parties that perform actions for you. In this case, Genkanban quietly runs on your computer, observing you. When your context changes (such as a task switch), Genkanban detects the change, calculates your new context, and relays that information on to other applications. For example, if I am switch from programming to writing, Genkanban can instruct iTunes to play something a bit more mellow and update my IM client’s status message accordingly.

Before I go any further describing the application, let me first define what I mean by context. The concept of context is easily understood, but sometimes difficult to define concretely. My own understanding of context is informed by the following definitions:

“By context, we mean environmental information that is part of an application’s operating environment and that can be sensed by the application.” Anid K. Dey (The Context Toolkit)

“Context-aware computing promises applications that sense an environment, model situations, and act appropriately.” James Fogarty (Subtle Toolkit)

“The circumstances that form the setting for an event and in terms of which it can be fully understood and assessed.” New Oxford American Dictionary

When Genkanban senses context, it is trying to identify a particular model of the world that surrounds a particular situation. Context is the evidence, and the situation is the conclusion. However, situation can be a complex item in itself, and to maintain my sanity, Genkanban predicts the location, activity, and social context properties of the current situation.

Returning to the application itself, it began life as a Mac implementation of the ideas expressed in James Fogarty’s Subtle Toolkit. At the core of Subtle is a machine learner that receives information from observers, and labels from its user. The machine learner treats the information from observers as features and the label as a classification. Using a collection of features and classifications, the machine learner constructs a model that can predict classifications from unlabeled lists of features. (For more information about the underlying idea behind classification, see the Wikipedia article on statistical classification.)

To train the machine learner, Genkanban implements a simple user interface that can be invoked as needed (the current prediction is incorrect) or on a fixed interval (“ask me about my context every X minutes”). This small interface collects labels and submits them to the learner to refine the learner’s mode. Users are not limited to a fixed set of labels, and can add new labels as needed.

Genkanban also adds some important functionality to basic ideas expressed in Subtle. Subtle used a relatively constrained interprocess communication (IPC) model where applications could synchronously query the user’s context using XML-RPC calls to a service running on the user’s local machine. Genkanban implements an asynchronous broadcast scheme where it automatically broadcasts a distributed notification of context changes that any application can listen to and adjust itself accordingly. (I’m using MacOS X’s NSDistributedNotification mechanism to accomplish this.) This approach eliminates any need for an application to poll for context, but applications using this IPC method still must be compiled to listen to for the notifications.

Genkanban also implements an action-oriented approach to disseminating context. Using the built-in AppleScript functionality, users can define actions as AppleScript files that are executed when the system predicts a new context. This is crucial for making third-party applications context-aware. The screenshot below illustrates a script that is used to set the status message of the Adium IM client when the local user’s activity changes. Genkanban ships with similar scripts for controlling other applications. These scripts are intended to be used as is, or as starting points for users wishing to customize the scripts for their own environment.

Like Subtle, Genkanban includes sensors that can observe various aspects of the local user’s environment. These sensors include wireless network detection, running applications, open websites, connected devices (USB and Bluetooth), and application-specific sensors such as current chat partner or current calendar event. Genkanban also extends this basic model by allowing other applications to contribute observations using the distributed notification approach. Currently, a Caller-ID application (Cidney, to be discussed in a future post) tells Genkanban when the phone is in use. A home automation application (Shion) tells Genkanban when local lights and appliances have been switched on or off. Finally, Genkanban implements an AppleScript interface where users can write their own sensors as scripts and use these custom sensors just as if they were compiled into the application.

Finally, the last major feature of Genkanban is its logging abilities. Since this is a research project for me, I need to collect information about its users to determine how it’s being used, if it’s accurate, and so on. Genkanban can be configured to log snapshots of the current environment at set intervals. (This functionality is disabled by default.) A logged snapshot consists of all of the observations at that point in time, the active predictions at that time, and an optional screenshot. For privacy purposes, users can elect to delete specific logs or disable logging for a fixed period of time.

This concludes the discussion about what Genkanban is. In a future post, I’ll describe how to use it.