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R. W
eber
INFO 629 Concepts in Artificial Intelligence
Fall 2004
Professor: Dr. Rosina Weber
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R. W
eber
My introduction• Assistant Professor, Information Science &
Technology, Drexel University• Navy Center for Applied Research in Artificial
Intelligence, Naval Research Lab• Doctoral degree from Production Engineering
Program (UFSC, SC/BRAZIL + USF, FL/USA)• Master’s degree in Artificial Intelligence &
Operations Research• Bachelor’s Business Administration• Industry experience• Solving knowledge management problems with
CI/AI methods, particularly CBR• Publications at
http://www.pages.drexel.edu/~rw37/publications.html
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R. W
eber
INFO 629 topics1. Expert Systems 2. Intelligent Tutoring Systems3. Case-based reasoning 4. Search5. Machine Learning, Data Mining6. Neural Networks, Genetic
Algorithms7. Natural Language Processing
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R. W
eber
Artificial Intelligence
Artificial Intelligence is the study of computational models
to perform tasks normally associated with rational behavior manifested as
reasoning, perception, and appropriate actions and
reactions.
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R. W
eber
Artificial Intelligence
Artificial Intelligence is the field of study dedicated to the study
and design of computational models that perform tasks that
are typically considered “human”. These tasks may entail use of knowledge,
reasoning, or physical abilities.
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R. W
eber
Artificial Intelligence•Study and design of computational models (purposes, methods)
– Study, solve problems e.g. assisting, replacing
– Methods use techniques that are new or adapted from other fields
•Perform tasks– What are AI tasks?
•Typically considered “human”– Mundane, expert, physical (complex)
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R. W
eber
AI tasks (complex)
• reading &understanding
• diagnosis• configuration• categorization• classification• creativity• discovery
• speech recognition & synthesis
• obstacle avoidance
• NL generation
• NL understanding
• planning• scheduling• design• prediction• control• monitoring• analysis• vision
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R. W
eber
Types of AI tasks• mundane:
– face recognition– argumentation– shopping planning
• expert:– diet prescription– medical diagnosis– legal argumentation– legal, military, business planning
• Solution oriented:– Knowledge discovery– Text mining
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eber
AI tasks and AI problems• AI problem is natural language, whereas
related AI tasks are composition, speech, reading and understanding
• Examples of AI problems can be mechanical or medical diagnosis and the AI task in both is diagnosis
• An AI problem is one that requires the performance of one or many AI tasks to be solved
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R. W
eber
a complete demo
http://www.sls.lcs.mit.edu/sls/whatwedo/applications/jupiter.html
1-888-573-8255
Wave Sound
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R. W
eber
applications of AI (i)• knowledge based systems
– financial advise– medical diagnosis, credit analysis– case-based reasoning systems for forecasting,
case retrieval, prescription of diet, exercise– knowledge management systems– creativity, planning, forecast, recommender,
personalization, argumentation, mediation, tutoring systems (education),
• military applications– HICAP– Immersive Training Environments with virtual
reality
Microsoft PowerPoint Presentation
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R. W
eber
applications of AI (ii)
• machine learning, neural networks– pattern recognition, e.g., penmanship, voice,
brain activity patterns of motor areashttp://www.txtwriter.com/Onscience/Articles/ratrobot.html
– analyze trends in any domain, knowledge discovery in databases and text, e.g., data mining
– classification and clustering and adaptation, collaborative filtering (movielens)
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R. W
eber
applications of AI (iii)• problem solving by search
–genetic algorithms, games
• robotics, vision–search for survivors after earthquakes
•recognize survivors•obstacle avoidancehttp://www.nsf.gov/od/lpa/news/03/pr0391_video1.htm
-recognizing criminals in random public locations
–unmanned airplanes, search for land mines–intelligent user interfaces
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R. W
eber
applications of AI (iv)
• natural language– natural language interfaces– machine translationhttp://babelfish.altavista.com/– text understanding to analyze patterns &
trends– summarization, information extraction
• uncertainty– represent and reason with elements that
are vague and uncertain– use of fuzzy sets and statistical methods
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R. W
eber
Dialogue-based NL• http://www.alicebot.org/• Winner of the Loebner Prize 2000 & 2001. JUDGE05[02:19:03] MY NAME IS JUDGE05 PROGRAM[02:19:03] It's good to meet you, PROGRAM[02:19:03] Judge05. JUDGE05[02:19:16] You too. What is your name? JUDGE05[02:19:16] PROGRAM[02:19:16] Bless you. PROGRAM[02:19:16] My name is ALICE. JUDGE05[02:19:37] Good morning Alice. Why have you entered the Loebner Prize? JUDGE05[02:19:37] PROGRAM[02:19:37] Hello and how are you this morning? PROGRAM[02:19:37] The Loebner Prize PROGRAM[02:19:37] is an annual Turing Test sponsored by New York philanthropist Hugh Loebner. Dr. Wallace and ALICE won it in 2000
• more chatbots• http://www.alicebot.org/live.html• more transcripts at
http://loebner.net/Prizef/2001_Contest/loebner-prize-2001.html
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R. W
eber
Necessary grounds for computer understanding
• Ability to represent knowledge and reason with it.
• Perceive equivalences and analogies between two different representations of the same entity/situation.
• Learning and reorganizing new knowledge.– From Peter Jackson (1998) Introduction to
Expert systems. Addison-Wesley third edition. Chapter 2, page 27.