1 Chapter 8 Prediction Algorithms for Smart Environments MavHome - U Tx Arlington.
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Transcript of 1 Chapter 8 Prediction Algorithms for Smart Environments MavHome - U Tx Arlington.
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Chapter 8Prediction Algorithms for
Smart Environments
MavHome - U Tx Arlington
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Smart EnvironmentsDesign & Implementation requires breadth• Integration of disciplines• Machine learning• Human machine interfaces• Decision making• Wireless network• Mobile communication• Databases• Sensor nets• Pervasive computing
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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
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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
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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
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Prediction Outcomes
• Determine relevant features
• Make maximum use of information
• Minimal prediction errors
• Minimal delays (quick predictions)
• Prediction Decision Making Algorithm
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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.
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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
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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
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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
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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
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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
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Other Prediction Approaches
• Decision Trees
• Neural Nets
• Bayesian Classifiers
• Nearest Neighbor Algorithm
• Support Vector Machines
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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
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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
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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
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AlgorithmsRepetition 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)
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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
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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+
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Conclusion• Comfort: minimize number of manual
interactions with environment
• Overview of Prediction