1. Collaborative eLearning Assistant Network Caring agents are conscious agents

2. Introduction • The team: Patrick Parslow, Shirley Williams, Will Browne • Contact details: p.parslow@reading.ac.uk • My Background – • Cybernetics, Computer Science, Civil Engineering(!)

3. Participation! • Huge topic - Machine Consciousness (MC) & eLearning • Philosophy, Pedagogy, Computer Science, Psychology, Sociology, Ethics, Communities of Practice… • Controversy about : • Whether MC is possible? • Whether MC is desirable? • Would MC improve an eLearning Assistant? • What is consciousness anyway? • So – I will be asking for your opinions during the presentation.

4. What do I mean, ‘Consciousness’? • It is hard to gain a consensus on what is meant by Consciousness – and hard to describe • Features of a conscious system, by my working definition: • Aware of surroundings • Aware of self (an autonomous entity distinct from environment) • Aware of others (as autonomous agents in the environment) • Holding a Theory of Mind of others • Having a Theory of Mind of self

5. How conscious can a computer be? • Not at all • Aware of surroundings • Aware of self • Aware of others • Fully

6. Why a conscious Assistant? • Self (1999) advocated caring intelligent tutoring systems • Learner models • Prediction • Adaptive • Conscious systems have • Theories of mind (models of the ‘other’) • Prediction • Adaptation • ‘Self’ awareness (!)

7. Hypothesis – consciousness is an emergent property • Based on a certain minimum functionality – Machine Consciousness Capable (MCC) • can recognise, classify, model, communicate and predict • Community • exist in an environment with others like them • Advantage • there is an ‘evolutionary’ advantage to modelling the ‘other’ • Model of self is a ‘freebie’ • A result of associating one’s own being with other similar agents • Using same processes that model ‘other’ to model ‘self’

8. Is it ethical? • No • If it can be proven safe • Human rights come first • If the MC has rights • Yes

9. Motivation • Motivation to use in eLearning • Caring agents need to be able to model and predict • Thus they need to perceive, recognise, classify • Learners exist in communities • Thus paired eLearning companions can exist in communities • The eLearning assistant works in a ‘symbiotic’ relationship • Benefits from providing the best advantage to its partner

10. Complications • Multiple strands of thought through different neural pathways • Only aware of one at a time • Multiple interests • Like to keep on top of them all • Multiple roles • In different contexts, family, social, academic, professional • Multiple domains means multiple ontologies • Or does it? Folksonomies and context awareness…

11. Complexity • To deal with the complicated, use complexity. • Not multiple MC agents, but multiple agents making up the machine consciousness • Accessing the same internal models • Communicating with the ‘user’ or learning partner • But also with other MC agents in a network • Bringing experience from other learners • Building and exploiting a trust network • Generating meaning through folksonomical activity

12. In pictures MC Agent Thought process Thought process Thought process Model Model Model

13. Supporting Connectivism… MC Agent MC Agent Thought process Thought process Model Model

14. Would a Machine ConsciouseLearning agent help? • No • Only some people • Many, but not all people • Yes

15. Context, Meaning, Community • First the “Alternative” view – Identity • Our roles in communities are given meaning by their context • Our identity is the aggregation of the meaning created • We define ourselves in the context of community • Our sense of ‘self’, • the conscious feeling we are who we • defined and refined through continuous comparison, evaluation • Consciousness takes time to develop

16. Context, Meaning, Community • All things our MCC agent needs to be able to model • All embodied to some extent in a folksonomy if : • it records when tags were created • it records who created the tags • it allows tags to be tagged • it allows all the users resources and contacts to be tagged • We are developing a folksonomical file system, FFS • Core technology behind the MeAggregator™, a JISC sponsored project.

17. MeAggregator™ • Designed to: • Interact with user-owned technologies • Build folksonomies • Provide a trust network - both permission and reliability • Allow peer-peer communication and publication • Run as a server or desktop solution http://meaggregator.googlecode.com/ • Chosen as a backbone because it provides • Ontology • Trust • Peer – Peer • Search

18. Thank you Any Questions?

19. Learner model • Building models of learning partner and self • Open learner modelling • User control • Reflective • Both learners, in partnership • User can maintain a model of agent • Helps agent learn about itself, its partner, and the relationship

20. CeLAN • MC agents can support multiple roles. • Given a priori domain knowledge, can be intructivist • Can work as a mentor • Can be motivational • In a network, is connectivist • My preference? • Research assistant – assessing sources for me • Conversational – seeming interested in what I am doing • Learns the subject area with me

21. Use case • Pat is researching Facebook and Blackboard, and searches for “VLE” • CeLAN observes him choose the last link on the results page • CeLAN “Why that link?” • I trust JISC • CeLAN adds resources and relationships to its model • resA: http://www.jiscinfonet.ac.uk/InfoKits/effective-use-of-VLEs • relA: Pat searchedFor VLE • relB: Pat choseLink resA • relC: JISC trustedWRT relA • relD: JISC relatedTo resA (etc.) • CeLAN interprets, and does a background search for “VLE JISC”