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The VoiceOver voice (you can choose from any of the built-in Mac OS X voices using the VoiceOver utility) will identify the control, and after a short pause will let you know how you can interact with that control. Try moving around the window using Ctrl+ +arrow keys, and VoiceOver will oblige you with a message as it highlights each one. Try adding and modifying records and opening the secondary windows. If you're feeling brave, try this with your eyes closed: it will give you a flavor of the user experience for someone with a visual impairment. It's great that you can do this, and control the application so easily with the keyboard, but the interface as it stands has some problems. Take the three buttons that are entitled Manage.... It might be obvious to a sighted person that each button is associated with the popup adjacent to it, but for someone with a visual impairment these buttons are indistinguishable. VoiceOver doesn t have the necessary information to let the user

Curriculum Vitae JUDE WESLEY GREEN 26 River Road Bainbridge, GA 31728 (912) 555-3973 Box 928, Bowdoin College Brunswick, ME 04011 (207) 555-0922

Reinforcement learning addresses the problem of learning control strategies for autonomous agents It assumes that training information is available in the form of a real-valued reward signal given for each state-action transition The goal of the agent is to learn an action policy that maximizes the total reward it will receive from any starting state The reinforcement learning algorithms addressed in this chapter fit a problem setting known as a Markov decision process In Markov decision processes, the outcome of applying any action to any state depends only on this action and state (and not on preceding actions:or states) Markov decision processes cover a wide range of problems including many robot control, factory automation, and scheduling problems Q learning is one form of reinforcement learning in which the agent learns an evaluation function over states and actions In particular, the evaluation function Q ( s ,a) is defined as the maximum expected, discounted, cumulative reward the agent can achieve by applying action a to state s The Q learning algorithm has the advantage that it can-be employed even when the learner has no prior knowledge of how its actions affect its environment Q learning can be proven to converge to the correct Q function under cera, tain assumptions, when the learner's hypothesis ~ ( s ) is represented by a lookup table with a distinct entry for each ( s ,a ) pair It can be shown to converge in both deterministic and nondeterministic MDPs In practice, Q learning can require many thousands of training iterations to converge in even modest-sized problems Q learning is a member of a more general class of algorithms, called temporal difference algorithms In general, temporal difference algorithms learn

by iteratively reducing the discrepancies between the estimates produced by the agent at different times Reinforcement learning is closely related to dynamic programming approaches to Markov decision processes The key difference is that historically these dynamic programming approaches have assumed that the agent possesses knowledge of the state transition function 6(s, a ) and reward function r (s,a) In contrast, reinforcement learning algorithms such as Q learning typically assume the learner lacks such knowledge

know which is which Similarly, if you move to the rich text field that this window uses for the journal content, VoiceOver can't tell you anything about it other than that it is a text area and you can type in it Refer back to the results we had from the Accessibility Verifier; those showed the same problems Fortunately, there is something we can do about this Quit the application and load up the NIB You might like to turn off VoiceOver temporarily, or you will find it will give you a running commentary on your work in Interface Builder! Open up the Journal Entries window and select the Manage.. button next to the client popup Now find the Identity tab in the Inspector, and in particular the Accessibility Identity section.

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