1. Agents, Knowledge, and Social Work: How People Interact With Complex Systems Stuart Watt Knowledge Media Institute

2. Overview of the presentation What is an agent? Interacting with agents Agents at the interface How people interact with agents Summary

3. What is an agent, anyway? Autonomous agents are computational systems that inhabit some complex dynamic environment, sense and act autonomously in this environment, and by doing so realize a set of goals or tasks for which they are designed – (Maes, 1995)... a hardware or (more usually) software-based computer system that enjoys the following properties: autonomy..., social ability..., reactivity..., pro-activeness... – (Wooldridge & Jennings, 1995) One who does the actual work of anything, as distinguished from the instigator or employer; hence, one who acts for another – OED two common uses of the word agent: 1) one who acts, or who can act, and 2) one who acts in place of another with permission. Since one who acts in place of acts, the second usage requires the first. Hence, lets go for a definition of the first notion – (Franklin & Graesser, 1996)

4. Agents: a new kind of medium Agents have two possible mediating roles –Mediating between a person and a program –Mediating between people collaborating The distinction between these may be blurred –To participants in interaction, it may not always be obvious what they are interacting with –Mailing lists Heterogeneous groupware

5. Agents in theory and in practice Many existing systems, few existing successes –Notable failures: Luigi et al., Bob –Notable successes: Windows 95, Active Archive Questions to answer –What makes them different to programs? –Why do they fail or succeed? –How can we design them so they dont fail? –When are agents better than programs? –Should we be using agent technology in the first place?

6. What does an agent look like? Agents can be: –Defined by their internal behaviour –Defined by their external behaviour –Defined by what people think of them They can be presented as: –A virtual person (e.g., Phil, Bob) –Anthropomorphic (e.g., Peedy the Parrot, Office Assistant) –Mechanomorphic (e.g., like a computer)

7. Inside agents: beliefs, desires, and intentions

8. Early interface agents: Apples Knowledge Navigator

9. Anthropomorphism

10. A collection of anthropomorphic interfaces

11. Fundamental tensions of agency: choice, control, and explanation Agents need to be infallible – Agent works success because of user – Agent doesnt work failure due to the agent People need to feel in control – Trust, responsibility, power, authority, accountability, privacy, respect, tolerance… How can we evaluate these? Agents are alien invaders!

12. Is Windows an agent? On the pros, Windows is: –Autonomous –Reactive –Persistent –Collaborative –Difficult to manipulate (rather easier to manage) –Delegated the task of looking after the hardware On the cons, Windows is: –Just not smart enough to be what we (usually) think of as agenty Now you decide...

13. A taxonomy of agents

14. Agents For learning with Different social roles –Agents as teachers –Agents as teachers assistants –Agents as assessors –Agents as students assistants –Agents as co-learners –Agents as facilitators Work with the existing social structures, not against them

15. Luigi and the least common denominator approach Design rationale: –Narrow, domain-specific assistants –Designed to overcome role conflict –Most burden on those who can gain the most –Little or no burden on those who gain the least What actually happened? –Technically, the system works fine –Usability si, Acceptability non –Who sends the email?

16. The Virtual Participant Use of Electronic Conferencing growing –Required component of many courses –Distributed student body –Supplement to tutorials This has problems –Not all students use it –It can be expensive –Some students have no other contact –It does not offer enough to students

17. Goals To re-use the knowledge contained in discussions from previous years; To reduce the load on the tutors from answering common problems; To encourage students to use the technology and participate To provide some support to students which is always available.

18. So how does it work? Another Participant –Exactly the same access as all students –No special hardware or software required Reads all messages in chosen conferences –Stores contents of every message –Stores History of every message Keyword and phrase matching –Stored from all messages in a thread –Considers all possible stories –Threshold for triggering

19. Example interaction

21. Choice highlights Questions we asked: –Should continue to be used: 90% agreed –Name: 79% agreed –Put me off: 11% –Reduce discussion: 9% agreed –Relevant: 95% agreed –Direct to participants: 15% agreed

22. The Active Archive Embedded in a conferencing system –Tracks threads of discussion –Posts relevant stories 4 dimensions –Anthropomorphism versus mechanomorphism –Private versus public –Closed versus open –Fixed versus extensible Social role: A bard

23. The importance of story-telling Story-telling systems –Conversational –Memorable –Easy to integrate with existing knowledge –Easy to integrate with conferencing Increasing engagement and motivation

24. KMi Planet

25. Open Book

26. Fundamental tensions of agency: control, and explanation Agents need to be infallable Agent works – success because of the user Agent doesnt work – failure due to the agent People need to feel in control –Trust, responsibility, power, authority, accountability, privacy, respect, tolerance... How can we evaluate/assess/theorise about these? –Theories of social cognition

27. Agent is a fuzzy category

28. Different stories, same system Internal viewExternal view

29. Social roles: agents as assistants Agents are often thought of as assistants –(e.g., Maxims, Maes, 1994) –Delegation is central –Agents are autonomous

30. Social roles and the theatrical metaphor Derived from Goffman, Parsons, Laurel Some typical social roles for agents –As assistants (e.g. Abecker et al., 1998) –As matchmakers (e.g. Foner & Crabtree, 1996) –As librarians (e.g. Watt, 1998) –As reporters (e.g. Domingue & Scott, 1998) –As editors (e.g. Domingue & Scott, 1998) –As critics (e.g. Fischer et al., 1990) –As oracles (e.g. Ackerman, 1994) –As bards (e.g. Masterton, 1997, 1998) –As Village gossips (e.g. Krulwich & Burkey, 1996, 1997)

31. The moral: finding the right balance Not all agents are assistants –Social rules for assistants may not apply Agents have an awkward line to tread: –Ignorance and informed –Responsibility and autonomy –Privacy and publicity –Trust and fear –Power and obedience Now, bearing all that in mind, should I use it? Dont expect cognitive theories to tell you everything!

32. Future opportunities Knowledge management –Open Book, the Virtual Participant, and beyond Automated assessment –Use of VP technologies, giving students additional feedback in formative assessment Engaging materials –Games use agents, engagement increases motivation, motivation increases retention Out of hours support –Agents can provide support outside working hours

33. Summary People treat programs psychologically rather than physically Agents and objects –Dont expect people to manipulate agents –Dont expect cognitive theories to be enough –Socialise computers, dont mechanise people Agents will work, if we: –Respect the complexity of human social life –Respect people