1. Ariel Rosenfeld, Amos Azaria, Sarit Kraus, Claudia V. Goldman, Omer Tsimhoni Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015

2.  CCS reduces ~10% of the car’s power efficiency!  Reduced ecological footprint.  Extending travel distance of EV.  Economically efficient. Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015

3. Driver’s and system’s goals are partially conflicting. Let’s minimize energy consumption... I’m Hot! Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015

4.  Repeated interaction  Drivers’ preferences.  Long-term effect of advice.  Changing environment.  Estimating expected energy consumption. Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015

6. Advice Controls Effects Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015 Agent Effects  Goal: minimize the accumulative energy consumption.

7.  CCS model.  Drivers model.  Environment model. Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015

8.  Collected 120 10-min energy consumption measurements.  e(T, F, D, M, E, I) = (w 1 T + w 2 F + w 3 D + w 4 E + w 5 I) ((1 + w 6 ) M)  T = Temperature  F = Fan  D = Direction  M = Mode  E = External temperature  I= Internal temperature Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015

9.  We recruited 38 subjects. (not that easy!)  Each subject spent 30 min. in the car, simulating 3 different trips.  Subjects were presented with different advice.  ML algorithm for extracting probabilities:  Drivers likelihood to accept an advice  Car’s condition likelihood to change. Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015

11. ~80% of drivers explicitly accepted. Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015

12.  78% accuracy (post-hoc).  Influential Features:  Current internal temperature.  Change from current setting (Reference point).  % of accepted advice (Trust).  Saving percentage (Expectation bias).  Not influential:  External temperature.  Average temperatures\fan.  Accepted deltas. Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015

13.  Optimization: Where: Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015

14.  MDP  Uses the predictions for the transition function.  State of the art – SAP (Azaria et al. 2012)  Considers the Social Utility of advice.  The weight provides a trade-off between short and long term gain. Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015

15.  45 drivers - 15 per condition, 3 rounds. The lower the better. Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015

16.  SAP was aggressive.  Some subjects stopped clicking on the advice. AgentAvg. go eco %Avg. save %Avg. consumption MACS0.83523.10.174 SAP agent 0.64133.70.237 Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015

17.  Presented MACS: an agent for providing advice for climate control systems.  Machine model.  Human model.  Environment model.  Finding the balance.  Human vs. Machine  Trust vs. savings.  Modifying drivers’ behavior. Advising Policy. Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015

18. Ariel Rosenfeld: arielros1@gmail.com Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015