EU Proposal Business Intelligence to Quickly Model Data

EU Proposal Business Intelligence to Quickly Model Data

Adaptive Advice in Automobile Climate Control Systems 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 Why bother? CCS reduces ~10% of the cars 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 Partially Conflicting Interests Drivers and systems goals are partially conflicting. Lets minimize energy consumption...

Im Hot! Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015 Challenges in Repeated Advice Provision in CCS in Real Cars 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 Climate Control System (In GM Chevrolet Volt 2011) Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015 Agent Advice Controls

Effects Effects Effects Goal: minimize the accumulative energy consumption. Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015 Three Models

CCS model. Drivers model. Environment model. Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015 CCS Model Collected 120 10-min energy consumption measurements. e(T, F, D, M, E, I) = (w1T + w2 F + w3 D + w4 E + w5 I) ((1 + w6) 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 Driver/Environment models 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 Cars condition likelihood to change. Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015 Presenting Advice to User

Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015 Presenting Advice to User ~80% of drivers explicitly accepted. Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015 Prediction of drivers reactions 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 Agents 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. (1 w) Uagent w Udriver Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015 Evaluation 45 drivers - 15 per condition, 3 rounds. KWH

The lower the better. Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015 Why Did MDP Outperform the SAP? Agent Avg. go eco % Avg. save % Avg. consumption

MACS 0.835 23.1 0.174 SAP agent 0.641

33.7 0.237 SAP was aggressive. Some subjects stopped clicking on the advice. Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015 Conclusions

Presented MACS: an agent for providing advice for climate control systems. Machine model. Human model. Environment model. Advising Policy. Finding the balance. Human vs. Machine Trust vs. savings.

Modifying drivers behavior. Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015 Thank you! Ariel Rosenfeld: [email protected] Ariel Rosenfeld et al. AAAI-15 (WAIT-15 workshop) @ Austin, TX USA. January 2015

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