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You can choose an individual table cell, in which case VoiceOver will read out the text only when you navigate into the table and are on a particular cell Note also that adding a title and description to the table column doesn t have any result in practice the user will never select a column Save the NIB, press F5 to turn VoiceOver on, and in Xcode Build and Run This time, when you navigate to this button VoiceOver will say "Manage Clients button" and after a short pause will follow this with "You are currently on a button To click this button, press Control-Option-Space The help tag is: Click here to bring up the Manage Clients window" Both of these spoken messages are backed by a HUD-type message window at the bottom left of the screen (see Figure 14 9).

Baird, L (1995) Residual algorithms: Reinforcement learning with function approximation Proceedings o the Twelfrh International Conference on Machine Learning @p 30-37) San Francisco: f Morgan Kaufmann

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If you navigate on to the Text View then you will get the relevant messages there too..

Barto, A (1992) Reinforcement learning and adaptive critic methods In D White & S Sofge (Eds), Handbook of intelligent control: Neural, fuzzy, and adaptive approaches (pp 469-491) New York: Van Nostrand Reinhold Barto, A, Bradtke, S, & Singh, S (1995) Learning to act using real-time dynamic programming ArtiJicial Intelligence, Special volume: Computational research on interaction and agency, 72(1), 81-138 Barto, A, Sutton, R, & Anderson, C (1983) Neuronlike adaptive elements that can solve difficult learning control problems IEEE Transactions on Systems, Man, and Cybernetics, 13(5), 834846 Bellman, R E (1957) Dynamic Programming Princeton, NJ: Princeton University Press Bellrnan, R (1958) On a routing problem Quarterly of Applied Mathematics, 16(1), 87-90 Bellman, R (1961) Adaptive control processes Princeton, NJ: Princeton University Press Berenji, R (1992) Learning and tuning fuzzy controllers through reinforcements IEEE Transactions on Neural Networks, 3(5), 724-740 Bertsekas, D (1987) Dynamicprogramming: Deterministic and stochastic models Englewood Cliffs, NJ: Prentice Hall Blackwell, D (1965) Discounted dynamic programming Annals of Mathematical Statistics, 36,226235 Boyan, J, & Moore, A (1995) Generalization in reinforcement learning: Safely approximating the value function In G Tesauro, D Touretzky, & T Leen (Eds), Advances in Neural Information Processing Systems 7 Cambridge, M A : MIT Press Crites, R, & Barto, A (1996) Improving elevator performance using reinforcement learning In D S Touretzky, M C Mozer, & M C Hasselmo (Eds), Advances in Neural Information Processing Systems, 8 Dayan, P (1992) The convergence of TD(A) for general A Machine Learning, 8, 341-362 Dean, T, Basye, K, & Shewchuk, J (1993) Reinforcement learning for planning and control In S Minton (Ed), Machine Learning Methods for Planning @p 67-92) San Francisco: Morgan Kaufmann Dietterich, T G, & Flann, N S (1995) Explanation-based learning and reinforcement learning: A unified view Proceedings of the 12th International Conference on Machine Learning @p 176-184) San Francisco: Morgan Kaufmann Ford, L, & Fulkerson, D (1962) Flows in networks Princeton, NJ: Princeton University Press Gordon, G (1995) Stable function approximation in dynamic programming Proceedings of the TwelfthInternational Conference on Machine Learning (pp 261-268) San Francisco: Morgan Kaufmann Kaelbling, L P, Littman, M L, & Moore, A W (1996) Reinforcement learning: A survey Journal of AI Research, 4, 237-285 Online journal at http://wwwcswashingtonedu/research/jair/homehtm1 Holland, J H (1986) Escaping brittleness: The possibilities of general-purpose learning algorithms applied to parallel rule-based systems In Michalski, Carbonell, & Mitchell (Eds), Machine learning: An artijicial intelligence approach (Vol 2, pp 593423) San Francisco: Morgan Kaufmann Laird, J E, & Rosenbloom, P S (1990) Integrating execution, planning, and learning in SOAR for external environments Proceedings of the Eighth National Conference on Artificial Intelligence (pp 1022-1029) Menlo Park, CA: AAAI Press Lin, L J (1992) Self-improving reactive agents based on reinforcement learning, planning, and teaching Machine Learning, 8, 293-321 Lin, L J (1993) Hierarchical learning of robot skills by reinforcement Proceedings of the International Conference on Neural Networks Littman, M (1996) Algorithms for sequential decision making (PhD dissertation and Technical Report CS-96-09) Brown University, Department of Computer Science, Providence, RI Maclin, R, & Shavlik, J W (1996) Creating advice-taking reinforcement learners Machine Learning, 22, 251-281.

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Luckily, there is a more useful view. At the bottom of the window, click on the Call Tree View button (second from left). Open up the call tree all the way down see Figure 12 7.

EDUCATION 1996 BA Bowdoin College, Brunswick, ME Major in African American Studies (GPA 37); minor in psychology (GPA 37) and computer science (GPA 36) Cumulative GPA 37 Candidate for high honors in African American Studies 1994 1995 Tougaloo College, Tougaloo, MS Studied linguistics, music, and anthropology Summer 1995 Yale University, New Haven, CT Studied psycholinguistics, African American literature of the Colonial Period, and computer applications for research in the humanities