ASP.NET PDF Viewer using C#, VB/NET

This plays out in the classic test of working memory, when a researcher presents someone with new information in the form of a series of unrelated numbers, such as 1 2 1 5 1 5 2 3 5 4. The number of these individual chunks of information that someone can recall is considered the capacity of that person s working memory. However, people can remember more of the same set of numbers when working memory pulls from long-term memory a structure they already know. This organizes the new information into meaningful chunks that hold the same information in a more memorable way, such as 212-555-1234 the familiar structure of a U.S. telephone number. Thus a chunk is de ned by the audience as they apply a meaningful structure from their long-term memory to new information. You help your audience accelerate understanding of new information with BBP by introducing a familiar chunking structure to new information you present. For example, in s 4 and 5, you choose a familiar motif that resonates with your audience, and then later in s 7 and 8, you extend the motif visually across the slides. See Also For more information about using familiar structures to overcome the limited capacity of working memory to process new information, see John Sweller, Implications of Cognitive Load for Multimedia Learning, in The Cambridge Handbook of Multimedia Learning, Richard E. Mayer, Ed., pp. 19 30 (Cambridge University Press, 2005).

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Again, by having the tiny piece of logic of choosing the best sentence in a separate method, you can change the way the program works without meddling with larger methods.

Now you have the sentence you want to parse and the substitutions have been performed. The next step is to find the phrases that are suitable as responses to the chosen sentence and to pick one at random. Let s extend response_to again:

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def response_to(input) prepared_input = preprocess(input.downcase) sentence = best_sentence(prepared_input) responses = possible_responses(sentence) end

Note You can pass state along with a function like this by using nested scopes as well, as discussed in

And implement possible_responses:

What Do I Sketch on Each Slide Sketching the Act I Slides Sketching the Title Slide Sketching an Optional Introductory Slide Sketching the Setting Slide Sketching the Role Slide Sketching the Point A and Point B Slides Sketching the Call to Action and Key Point Slides Tripling Your Impact with Three Panels Propping It Up Adding It Up with Icons Using Video, Sound, or Motion Graphics (Carefully) Blacking Out the Screen Raising Interest with Your Layouts Keeping the Motif Flowing Integrating the Interaction

def possible_responses(sentence) responses = [] # Find all patterns to try to match against @data[:responses].keys.each do |pattern| next unless pattern.is_a (String) # For each pattern, see if the supplied sentence contains # a match. Remove substitution symbols (*) before checking. # Push all responses to the responses array. if sentence.match('\b' + pattern.gsub(/\*/, '') + '\b') responses << @data[:responses][pattern] end end # If there were no matches, add the default ones responses << @data[:responses][:default] if responses.empty

# Flatten the blocks of responses to a flat array responses.flatten end possible_responses accepts a single sentence, then uses the string keys within the :responses hash to check for matches. Whenever the sentence has a match with a key from :responses, the various suitable responses are pushed onto the responses array.

The following two statements are (more or less) equivalent: def foo(x): return x*x foo = lambda x: x*x

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This array is flattened so a single array is returned. If no specifically matched responses are found, the default ones (found in :responses with the :default key) are used.

You now have all the pieces available in response_to to put together the final response. Let s choose a random phrase from responses to use:

def response_to(input) prepared_input = preprocess(input.downcase) sentence = best_sentence(prepared_input) responses = possible_responses(sentence) responses[rand(responses.length)] end

More than One Speaker Going Back to A and B Sketching an Optional Navigation Bar Sketching the Explanation Slides Explaining Ideas by Building a Diagram Building a Chart Across a Series of Slides Sketching a Visual Organizer Completing the Explanation Slides Using a Screen Capture Sketching the Detail Slides Switching to Other Media Adapting Your Sketches to Your Profession Sketching Your Story Template Ready, Set, Sketch!

If you weren t doing any substitutions against the pronoun-switched sentence, this version of response_to would be the final one. However, your bot has the capability to use some of the user s input in its responses. A section of your dummy bot data looked like this: