“How can I learn AI?” Lindsay Evett, Alan Battersby, David Brown, SCI NTU Penny Standen, DRA UN

Application-Based Teaching Relevance Active enquiry and exploration Can be case based Constructivist Kolb Accommodating (Concrete Experience/Active Experimentation) Supports more traditional methods (lectures, seminars) – blended learning

Real Applications Real applications which are publicly and easily available Some demonstrable success More convincing than text book toy/engineered examples While evaluation data often lacking they are braving the open market

Current Practical Work Chatbots Topic bridges AI and NLP; so highly suitable for my AI&NLP module Applications – interfaces in general, IKEA call centre, search engines, Virtual help/assistant, naughty chat lines…… Mostly work through pattern matching

Chatbot methods? Available chatbots methods elusive Jabberwacky says no single recognised AI technique – complex layered heuristics Others perhaps some form of knowledge bases to produce learning, personalities, knowledge, unclear how

Coursework Requirements The coursework requires students to use simple forms of AI&NLP techniques to improve conversation of a simple, ELIZA type, pattern matching Chatbot The Chatbot provided has optional –Speech output –Lexicon Lots of scope

Learning Outcomes Use, apply and critically evaluate major techniques used in AI and NLP Analyse, design and develop AI computer applications Solve problems using AI and NLP techniques Use and apply basic algorithmic and design approaches

Future Developments Intelligent virtual tutors Could be knowledge based Could be proactive (need to identify situations and act appropriately) Could have conversations, discussions, answer questions (Chatbots incorporated) Plenty of scope

Intelligent Virtual Tutor Agents Unobtrusive but reactive/proactive tutoring agents Monitor student actions and visit when need arises, giving advice and/or instructions Can need knowledge for monitoring

Types of Tutors Deductive tutor agents: give advice on deductive reasoning, (e.g., NDSU Geology Explorer a. Equipment Tutor b. Exploration Tutor c. Science Tutor Case-based tutor agents: present relevant cases/experience (e.g., video to demonstrate experimental procedures (Yu et al 2005))

Types of Tutors (contd.) Rule-based tutor agents: a. encode set of rules about domain. b. Monitor student actions for broken rules c. Visit student to provide expert dialogue or tutorial Navigational tutor agents: supply context dependant information to aid navigational tasks (e.g., Quest

Develop Tutors Virtual Health Clinic Currently presents information as text when necessary (information buttons) Clinic as environment, many opportunities for simple interventions Receptionist ++…….?

Other Suitable AI Applications? Game AI – many different methods involved Speech XML Tagging, Data Mining Knowledge tools – search engines, Semantic Web, Ontology tools Pattern recognition Robots – toys, prosthetics NB evaluation of many applications is lacking

Conclusions Quite a few Weak AI successes Mostly procedural Not really intelligent? A few have a range of methods Becoming part of the pervasive background Soft computing type systems as the basis for developing higher order cognition?