Ariel Rosenfeld, Amos Azaria, Sarit Kraus, Claudia V. Goldman, Omer Tsimhoni Ariel Rosenfeld et al....

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

CCS reduces ~10% of the car’s power efficiency!

Reduced ecological footprint.Extending travel distance of EV.Economically efficient.

Why bother?


• Driver’s and system’s goals are partially conflicting.

Partially Conflicting Interests

Let’s minimize energy

consumption...I’m Hot!


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.


Climate Control System (In GM Chevrolet Volt 2011)


Advice

Controls

Effects


Agent

EffectsEffects

Goal: minimize the accumulative energy consumption.

CCS model.Drivers model.Environment model.

Three Models


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 = TemperatureF = FanD = Direction M = ModeE = External temperature I= Internal temperature

CCS Model


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 Car’s condition likelihood to change.


Presenting Advice to User


Presenting Advice to User

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

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.

Prediction of drivers reactions


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.

Agents

driveragent UwUw )1(


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?

SAP was aggressive.Some subjects stopped clicking on the advice.

Agent Avg. go eco % Avg. save % Avg. consumption

MACS 0.835 23.1 0.174

SAP agent 0.641 33.7 0.237


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.

Conclusions

Advising Policy.


Thank you!

Ariel Rosenfeld:[email protected]


mailto:[email protected]

Ariel Rosenfeld, Amos Azaria, Sarit Kraus, Claudia V. Goldman, Omer Tsimhoni Ariel Rosenfeld et al....

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