Chapter 8 Prediction Algorithms for Smart Environments

1. Chapter 8Prediction Algorithms for Smart Environments MavHome - U Tx Arlington

2. Smart Environments Design & Implementation requires breadth • Integration of disciplines • Machine learning • Human machine interfaces • Decision making • Wireless network • Mobile communication • Databases • Sensor nets • Pervasive computing

3. Benefits of Automation • Convenience • Turn off coffee, warm up car • Conservation • Manage heat & cooling, lawn watering • Do actual work -- Order groceries, vacuum carpet See "Bob's Day" -- pg. 175-176

4. Role of Prediction Goals of Smart Environment: • Maximize comfort • Minimize costs • Adapt to inhabitants To Attain: • Use tools from artificial intelligence • Prediction, automatic decision making

5. Prediction • Learn about devices by observation • At certain temperature, how long to warm house • Utilization of resources, also • To cool house, turn on ceiling fan and/or close blinds • Predict inhabitant's behavior • Hardware: video, power meters, motion detectors, load sensors, device controllers, vital sign monitors • Software: prediction algorithms

6. Prediction Outcomes • Determine relevant features • Make maximum use of information • Minimal prediction errors • Minimal delays (quick predictions) • Prediction Decision Making Algorithm

7. Prediction Algorithms * NOTE: Smart environment development is not all hardware * Prediction Task: process of forming an hypothesis representing the future value of a target variable for a given data point. Prediction Algorithm: learns a function that maps known information collected from past/current observations to future point in time.

8. Prediction Algorithms • Based on sequential ordering of events; input to algorithm (plus maybe timing) • Historical information + current state => prediction • Given event sequence {x1, x2, x3 …. Xj}, what is event xj+1? • Approaches • Pattern matching (sequence) • Markov Decision Process • Plan recognition

9. Sequence Matching - IPAM • Just one example algorithm • Collects sequential pairs; calculates probability of transitioning from one event to next • e.g. {a, b, c, b, c, b, a, a} • (a,b) (b,c) (c,b) (b,c) (c,b), (b,a) (a,a) • p (a,b) = 1/7 • p (b,c) = 2/7 • p (a,c) = 0, etc. • But, probabilities change over time and are kept in a table

10. IPAM (continued) • When new event xj+1 is observed • p (xj, xj+1) increases by factor of (1-α) • For some constant α • All other p (xj, z) reduced by factor α • Weights recent events more heavily • Rank events by probability and prediction p (xj+1| xj) • Sequence matching algorithms: applied to UNIX command prediction

11. Markov Decision Process (MDP) • At each step, agent perceives environment state, selects action • Probability model + possible reward • Only last few stated used • Unlike pattern matching • Hidden Markov Models (HMM) • Observable vs. hidden states • Figure 8.3, pg. 179 • Hidden: current task (in chair sleeping or reading) & health (feel good or tired) • Also probabilistic

12. Plan Recognition Prediction • Given a known goal, recognize possible plans to achieve • e.g. goal: cool house • Plan 1: turn on air conditioner • Plan 2: turn on air conditioner and ceiling fans • Based on Belief Network • DAG: nodes are RV, edge indicates influence • Figure 8.4, pg. 180 • Includes evidence to support plan generation

13. Other Prediction Approaches • Decision Trees • Neural Nets • Bayesian Classifiers • Nearest Neighbor Algorithm • Support Vector Machines

14. MavHome - Smart Home - UTA • Designed as an intelligent agent • Goal: comfort of inhabitant & minimize cost of running home • Predict, reason, adapt • Figure 8.5, pg. 181 -- intelligent agent • Figure 8.6, pg. 182 -- architecture • 4 layers • Decision, information, communication, physical

15. MavHome - 4 Layers • Decision: selects actions for agent to execute • Information: collect information, generate inferences for decision making • Communication: routes information and requests between agents • Physical: contains hardware -- appliances, network, sensors, etc. • Bottom-up Process, pg. 182

16. Identifying Events • Need to identify repetitive tasks for potential automation • Need to predict next action • MavHome: prediction solely on previous interaction with devices plus current state • Prediction to decision – • algorithm that selects action to execute

17. Algorithms Repetition modeled as stationary, stochastic process 3 Algorithms: • ED (Episode Discovery): identify sequences of regular & repeatable actions that could be used to predict - thru Data Mining • Active LeZi: uses sequence matching to predict next action • MDL (Minimum Description Length) Principle: pointer to database description of patterns (compression)

18. Experiment with Algorithms • 30 days of data with noise • ED discovered 6 significant episodes (pg. 184) • Use of the knowledge? • Provides understanding of nature of home • Patterns will be used in decision making • Can improve prediction accuracy

19. Active LeZi • Based on Ziv - Lempel compression (LZ78) • Good compression good prediction • LZ78 enhanced to improve prediction • Calculates probability of each action occurring in a sequence & predicts one with highest • Accuracy ≈ 48% • With random choice 2% • MavHome - Figures pg. 189+

20. Conclusion • Comfort: minimize number of manual interactions with environment • Overview of Prediction