AIMA-java: JavaBean Demo

• Install aj.tgz somewhere, call that directory AIMA_HOME
• Add ${AIMA_HOME}/classes to your CLASSPATH
• Start sun's beanbox from ${AIMA_HOME}/src (see note.) To do this:
  • Get the BDK from http://java.sun.com
  • Unpack the BDK distribution. This will create a "beans" directory.
  • Add beans/beanbox/classes to your CLASSPATH
  • Do: java sun.beanbox.BeanBoxFrame
  • WARNING: If you used sun's run.sh script it will clobber your CLASSPATH
  • NOTE: The directory you start the beanbox from is relevant only in the following indirect way: Let DIR be the directory from which you start the beanbox. Then the beanbox looks for its jar files in ${DIR}/../jars
• The beanbox should consist of 3 windows:
  • The main window (has menus File Edit View)
  • The Beans list (should contain Beanbox, RiskNoRiskGame, QLearning_Agent)
  • An attribute list for BeanBox (which is itself a bean, and which has focus as default)
• Click on RiskNoRiskGame in the beans list, and click somewhere on the BeanBox.
  • Wait for the RiskNoRiskGame attributes to pop up. (This may be essentially instantaneous to painfully slow: PPro200 vs. P75)
  • Set discountFactor to whatever you want.
  • Set scoreDecay to whatever you want. (The performance measure for the agent is an exponentially weighted average of correct/incorrect decision indicator variable.)
• Do the same for for QLearning_Agent, BUT: after clicking on the Beanbox, make sure to resize the bean to some reasonable, larger, size. (It defaults to being 1 pixel big, I haven't figured out yet how to have it not do that)
  • Set numActions to 2
  • Set learningRate to whatever.
  • Set explorationRate to whatever.
  • Do *NOT* set state to anything besides -1. (My code is still rather fragile)
  • Set the discount factor to the same as RiskNoRiskGame's.
  • Set numStates to 3
• At this point, focus should still be on the Q-agent.
  • Pick the menu item: Edit -> Events -> intAction -> intActionPerformed
  • This should cause a rubberband to appear from the Q-agent. Click on the RiskNoRisk Game.
  • This should cause a "please choose a target method" window to come up. pick "intActionPerformed" and click OK.
  • A "generating and compiling adaptor classes" window will pop up. Wait for it to finish.
• Focus should now be on the RiskNoRiskGame. Repeat a similar process as above, except choose -> StateReward_Percept -> perceiveStateReward.