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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

Algorithms Repetition modeled as stationary, stochastic process 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)

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

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+

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