1. Alina Pommeranz, MSc in Interactive System Engineering supervised by Dr. ir. Pascal Wiggers and Prof. Dr. Catholijn M. Jonker

2. Content Introduction Pocket Negotiator Research Goals from HCI perspective First Experiment User-centered Approach Questions and Suggestions

3. About myself Education 4-years Computer Science, FH Gelsenkirchen, Germany 2-years Master of Science in Interactive Systems Engineering, KTH Stockholm, Sweden Previous Research 1 year at the Swedish Institute of Computer Science (SICS)

4. Negotiation support Negotiation is a common task, but still only few people are good negotiators Computers can better cope with the computational complexity (many options, combinations of variables, solutions) Negotiation cannot be handled by artificial intelligence alone (semantic problem, emotional issues involved)

5. Pocket Negotiator (PN) new type of human-machine collaborative system that combines the strengths of both [human and system] and reduces the weaknesses. (Jonker, 2007) The PN will handle computational complexity issues provide bidding- and interaction advice the user will handle background knowledge interaction with the opponent negotiator

6. Pocket Negotiator (PN) handheld device 2 domains: real estate job negotiations for non-expert users

7. Issues from a Man-Machine Interaction point of view Preference Elicitation User preference model Interaction between the system and the user (generic task model) Explanation/Training Module (use of animated character) Visualization techniques (for small devices)

8. Preference Elicitation Experiment Goal: investigate preference elicitation techniques involving different research areas (AI, HCI, Affective Computing) Evaluation of user satisfaction (liking, ease of use, intuitivity) with different ways of giving preferences Holiday domain 32 participants 8 ordering/rating tasks (including affective feedback and navigational interaction), 2 comparisons and a questionnaire Lexicographic ordering Paper submitted to HuCom 2008

9. Experimental setup active Hotel l Active

10. Results Traditional property rating seems most preferred and resulted in one of the best orderings of the outcomes space when using the lexicographic algorithm Properties of holidays are interdependent for more than a 3rd of the participants Considering affective attitude can improve understanding of users preferences With a multi-angle approach we could identify limitations and issues that would have not been found with just one research perspective

11. How to use the results? When designing a new preference elicitation method we need to consider dependencies of preferences navigation task is suited to find a number of such dependencies rating of properties should be done in known and preferred ways (Likert scale) we could incorporate ways to express affect to properties/outcomes a combination of different techniques might be the way to go (needs to be tested)

12. Limitations of the experiment Complete ordering of holidays as control condition small set of properties and their values Rating and ordering tasks very quick and with little effort Strengths of other techniques in the background

13. User-centered approach I Video-supported study on social acceptance, practices and use-contexts (scenarios) Case studies with potential users including Observation of real life negotiation situations (not only bidding but all other phases, too) Capturing such situations on camera Maybe physiological measures (heart rate, GSR etc. to measure emotions) Questionnaires and interviews before and after the negotiation phases Diaries by the users Interviews and Focus groups with experts in the domains (e.g. real estate agents, P&O members)

14. User-centered approach – II Design of Negotiation Scenarios based on the case study data and used in further experiments Iterative implementation of prototypes Wizard-Of-OZ experiments to test (with prototypes): Preference elicitation techniques User-system interaction techniques Visualization techniques Explanation/ training module

15. Questions? Suggestions? Thanks for your attention!