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November 04, 2006
When in doubt, do some math
A couple of the papers I've read recently (well, re-read in one case, lol) both discuss the mathematical foundations for intelligent behaviour.
- Using planning techniques in intelligent tutoring systems (Peachey & McCalla, 1984-85)
- Foundations for the Situation Calculus (Levesque, Pirri & Reiter, 1998)
Both discuss the use of predicates for notating "states", as I think of them. That is, "Student knows concept A" -- SK(conceptA) or "John is a human" -- human(John). I'm still foggy on existential localities, like, "Where is the object of focus in the predicate's assertion? In the function, or in the argument?" Perhaps I am confused because I am focused on the flesh-and-blood people. In the first one, the person is in the predicate itself - "*Student* knows" - but in the second one, the person *John* is not the predicate, but rather he is the object.
After pondering the problem for a moment, it seems to make sense that a predicate is the state of being that the object can be. So, the conceptA is "in the state of being known by the student" and John is "in the state of being human". I guess this means that the flesh-and-blood person can be either the object or a part of the meaning of the predicate; it's just a matter of context. I trust that this will make more sense as I keep going. In elemenatry school, I never really did understand what my teacher was talking about when she mantra-ed, "Every sentence has a subject and a predicate!" 'Bout time I dealt with that. Onward, ho!
I will now attempt to make a connection between situation calculus and the semantic web.
The first time I heard the word 'Ontology', I was sitting in a second-year philosophy class entitled 'Introduction to Metaphysics'. The second time I heard the word, I was sitting in a weekly meeting with my honours degree supervisor and he told me about the field of study called 'ontological engineering', or the work of organizing knowledge into meaningful structure. (See my other post about ontological engineering.) I wonder how these studies from the Arts, Sciene and Engineering relate to the field of Education. Curriculum studies, maybe? I wish I knew more about education.
In an earlier post back in July, I discussed a sample Semantic Web ontology about Grade 11 Chemistry. How do the ontological statements in this specification relate to situation calculus?
Well, the components of Grade 11 Chemistry are written in OWL - Web Ontology Language - which is an extension of RDF - Resource Description Framework, which in turn is a language composed of triples of subject-predicate-object s.
There, I found it!
. . .
Harumph. My Grand Conclusion Of The Day seems rather obvious in retrospect. Sigh.
Back to the math. Given that we do have these philisophical "hooks" and can grab content and relationships out of the 'Web, what can you do with that power? This is the fuel, and we have it in abundance, but what is the engine? What are the applications of situation calculus in artificial intelligence?
I should know the answer to that - I really should. Why didn't I study more AI while I was doing my degree? Sigh.
My AI textbook (well, my brother's actually) has about 8 pages on the subject. Gah, and it's almost noon already. Enter image of Stephanie doing dishes with AI textbook propped up at the corner of the kitchen sink.
Posted by Frozone Permalink on November 04, 2006 11:48 AM
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Index to Steph's Notes
Feb. 24th 2007 - Weee! This new part of my website is not an entry, but rather a permanent fixture whose purpose is to "Look Down on All Those Notes With Some Grand Vision of Organization". Wish me luck. LOL- Representing meta-data (fuel) & the different kinds of "hooks" that intelligent systems can use (how fuel is injected into the motor of the engine)
- Motivation: Semantic net / Rationalizable to a machine
- Semantic network
- Genetic graph
- Prerequisite AND/OR graph
- Constraint Satisfaction Problems
- Bayesian networks / causal graphs
- Technology & Philosophy: RDF, modus ponens,
- Predicates, Logic & situation calculus
- What kinds of data? - What kinds of meta-data would an AIEd system possibly need, and how is it represented?
- task domain knowledge
- "is-prerequisite-to"-type knowledge
- interactions with learning objects & other learners - (location, composition is-a/part-of, sequencing by restricting navigation, personalization, ontologies for LO context)
- lesson plans, curriculum plans, practicing sessions (What is stored, what is generated on the fly? What is remembered?)
- How to organize it - When is it stored in a database? Meta-data? Agent memory banks? Protocols? Repositories? XML files? Home-servers? WSDL services? Frameworks? Portable banks? P2P access?
- Database of object-agent interactions
- Concept of "Home" on a P2P network -- maybe the bulk of a learning object's usage data is on its home server and can be queried using WSDL or something ? Similar homes for each student's usage history, etc. Baggage problem.
- Links to the ontologies
- referring to a concept/relationship - ex. AgentOwl?
- Generation of this data
- Rationalization: For use by other AIEd systems
- What is generated - discuss items under part I.C.
- When it's generated - describe procedural model, which parts of the engine generate what (isa-part-of data, XML feeds, web services, meta data bout groups and collaboration, protocols, examples Friend of A Friend FOAF project)
- Technical notes of HOW it's generated: JENA, issues of implementation demo, my Hermione & Ron agent examples, lol
- Usage of this generated data - see part IV. A.
- Given the engine, who uses it?
- Students / Learners / "Me"
- instructional planning, student model, pre-requisites, tutoring, coaching, collaboration,constructivism
- Teachers / Educators / "Me"
- putting together lessons
- be able to browse through task domain knowledge in an objective / encyclopaedia format, then be able to pick-and-choose what you need for your students
- compose examples, design explanations, pull together diagrams, learning objects, etc. Haystack Relo?
- Administration / Governement / Structure / Crowd Control
- as restrictions/obstacles/sand pit to the robot in agent environment
- can't just have a swarm of students and teachers out there -- need structure of courses, curriculum, objectives, requirements (at least, we do in this day and age!) - Report cards, evaluation, feedback
- government, marks, certificates, requirements, funding, curriclum, attendance, delinquent, non-attending, motivation
- school''s images, goals, strengths, payroll, HR, security, accounts, permissions, privacy
- registration, failed courses
- User Environment -- How does this engine work? What does the user see on the screen?
- Introduction - Given a background in educational psychology, how does the system present itself -- what does the user see, and were does this data come from? Links to thoughts from part I.)
- Task Domain Browsing - Suppose you're you're just idly browsing through the "raw" content. How would it look when it's not wrapped around a learning-context or lesson or tutorial or anything. 'Cross between browsing a raw task domain ontology and browsing a learning object repository.
- Cleaning up the data -- Visualizing the data for humans to pick through the task domain and work on it. Suppose the "Subject Expert" discovers an advancement in science and needs to update the "world's" domain knowledge. (I used the "Subject Expert" terminology from Ontologies to Support Learning Design Context - Thanks Chris) How would they make corrections to ontologies and learning objects, or at least point the users of "old" objects towards adopting the newer ones.
- "Modes" - Learning & Lessons / Checklist - Homework, Assignments, Courses being taken / Collaborative mode / Teaching mode / Calendar- email -adminisrative mode -- See also the different kinds of scenarios in the ActiveMath system
- Evolution of this engine
- target some key implementation hooks discussed in part I - design an experiment/demo
- scrape a page - (Note, scraping can only give objective data, not in-context dat)
- LO repository - related to browsing the task domain?
- a learners "To Do" list - where does it come from? Assignments, courses.
- sample group scenario
- sample teacher lesson planning
- sample data "left behind"
- sample use of that data
- Data mining (for what? lol )
- discovery / generation of ontologies - when do you need to hunt for them, and when do you have to have a solidly-known & predictable ontology?
- I/O - where it happens, which languages, protocols, which agents perform i/o and when, precepts, actuators
- Role Assignments
- My Environment Adapts to me
- Displaying feedback from the server on JSP pages (Software engineering considerations)
- Sketching out a design (Content planning vs. Delivery planning)
- agent negotiations / social structures / ummm... Web 2.0 ?
- garbage collection of meta data
- Artificial Intelligence & Evolution
- Memory Culling: Necessary part of intelligence? (artificial or human)
- Applications for the Genetic/Evolutionary algorithm
- open learning environments
- Agents, pets, grouping, Community modelling
- Protocols - finding groups, cyber dollars, state diagrams (?)
- "Community Studies" - graphs & communication hubs, types of communities (free-for-all, hierarchy of authority, etc.)
- implications of joining a community - what do you share, which parts of your student model are relevant
- Walls & sand traps -- deliberate restrictions as problem-solving for learning
- Communication channels - individual-to-individual, individual-to-community, chat channels, agent-only "administrative" communications, ex. requests for related learning objects in a particular community, etc.
- Educational/Pedagogical focus (this part probably shouldn't be its own section but rather incorporated into the whole picture, but it's separate for me right now because I'm still only just starting to learn about it.)
- Semantics - what there is to talk about in Education
- ex. Merril's First Principles of Instruction, linking educational terms to AI terms
- Pedagogical skills for tutors -- supporting human *and* artifical tutors
- Student modelling - what the machine needs to know about the student, pedagogically-speaking, about learning history/preferences
- Roles - Simulated students, Coaches, Tutors, Teachers,
