Download 슬라이드 1

yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts
no text concepts found
의미 모델링
Elaborating Sensor Data using Temporal and Spatial
Commonsense Reasoning
Mining Models of Human Activities from the Web
지능 기반 시스템 응용
2006. 11. 민준기
 B. Morgan and P. Singh,
“Elaborating Sensor Data
using Temporal and Spatial
Commonsense Reasoning,”
BSN 2006.
 M. Perkowitz, et al., “Mining
Models of Human Activities
from the Web,” WWW 2004.
The Problem Space
Proposed Technique
LifeNet : A First-Person
Summary and Future Work
The Plug Sensor Network
The Problem Space
 Two distinct directions for research
Human-out (This paper)
Technology-in (Much sensor network research)
Text messaging on cell phones
 Three topics
LifeNet probabilistic human model
The Plug sensor network
An experimental design for evaluation of the LifeNet
learning method
LifeNet : A First-Person Model
 First-person common-sense inference model
OpenMind Common Sense, ConceptNet, The PlaceLab data,
Honda’s indoor common sense data
 Attempts to anticipate and predict what humans do in the
 All of the reasoning in LifeNet is based on probabilistic
propositional logic
“I am washing my hair” before “my hair is clean”
The Plug Sensor Network
 Using for both learning common sense and for
recognizing and predicting human behavior
 Using this sensor network to monitor how individuals
interact with their physical environment
 Nine sensor modalities: sound, vibration, brightness,
current, wall voltage, acceleration
 B. Morgan and P. Singh,
“Elaborating Sensor Data
using Temporal and Spatial
Commonsense Reasoning,”
BSN 2006.
 M. Perkowitz, et al., “Mining
Models of Human Activities
from the Web,” WWW 2004.
The Problem Space
Proposed Technique
LifeNet : A First-Person
Summary and Future Work
The Plug Sensor Network
Introduction : Recognize Humans Activities
 Applications include activity-based actuation
Dimming lights when a video is being watched
Providing directions for someone using unfamiliar facilities
 Ubiquitous, proactive, disappearing computing
Computers have to understand people’s needs by observing
their physical activities (and to act autonomously)
The cost of developing recognition infrastructure is too high
Even small classes of activities is hard to recognize
 A broadly applicable system should be general-purpose
and easy to use
 Vision based systems
None have reported detecting more than tens of activities in
The features robustly detectable from vision are coarse
Represent the relationships between “blobs” in the image
rather than specific objects
Each activity is expensive to model
 Learning of the models
The developers define the structure of the possible models
System tunes the parameters of the model based on examples
from the user
The user is expected to label the patterns
The variety of activities is quite restricted
Proposed Technique
 RFID (Radio Frequency Identification)
Cheap: Postage-stamp sized, forty-cent
Wireless and battery free
 Activity modeling
Define an activity in terms of the probability and sequence
of the objects
Generate the models by translating textual definitions
Structured like recipes
Produced automatically by mining appropriate web sites
Mining models is part of a larger activity recognition system,
PROACT (Proactive Activity Toolkit)
Usage Model
Proposed Technique
 Assumes that interesting objects in the environment
contain RFID tags (tens ~ hundreds)
Making a database entry mapping the tag ID to a name
Within a few years, many household objects may be RFIDtagged before purchase, thus eliminating the overhead of
Medium-range readers (Tag-detecting Gloves) and
Long-range readers (Run robots, Carts, …)
 PROACT uses the sequence and timing of object to
deduce what activity is happening
Likelihood of various activities, details of those activities,
degree of certainty, etc…
System Overview
Proposed Technique
 PROACT provides an activity viewer for debugging
Real-time view of activities in progress
The sensor data seen
Changing of belief in each activity with the data
 Inference Engine converts the activity models produced
by the mining engine into Dynamic Bayesian Networks
D. Patterson, L. Liao, D. Fox, H. Kautz, “Inferring High-Level
Behavior from Low-Level Sensors,” Ubicomp 2003.
Sensors and Models
Proposed Technique
 Sensors
Use two different kinds of RFID readers
Long-range reader (mobile robot): map the location of objects
Short-range reader (glove): determine the objects that are
 Models
Each model (activity) is composed of a sequence (step) s1
Each step si has optional duration ti and object oij involved
along with the probability pij
The Model Extractor
Proposed Technique
 Builds formal models of activities using directions
 Directions are written in natural language by human
How-to (, recipes (, training
manuals, protocols, etc.
 Syntactic structure of directions
1. A title t for the activity
2. A textual list r1~rm, Each step ri has:
Possibly a special keyword delimiting duration di
What to do during the step: subset of the objects and duration
Proposed Technique
Converting Directions to Activity Models
 Key steps
1. Labeling
Set label of the mined model to title of the directions
2. Parsing steps
Duration: Gaussian with mean = d, stdev = S(d, i, l )
Object Oi and Probability P
3. Tagged object filtering
For example,
[“making tea”] has 24,200 matches, and
[“making tea” cup] has 7,340 matches, then
conditional probability of a cup being involved in
making tea is 7340/2400 = 0.3
 Functions
Object extraction: WordNet ontology
Noun-phrase extraction: QTag tagger
Fixed probabilities
Google conditional probabilities (GCP)
Proposed Technique
 Mined models 2300 directions 400 recipes 18,600 recipes
 Three strategies to approximate comprehensive
Human activity-trace recognition
Activities of Daily Living (ADLs)
Inter-corpus consistency
Making cookies recipes
Intra-corpus distinguish-ability
Distinguish-ability within activity domains
Human and inter-corpus trace recognition
 ADLs domain
Many objects were not tagged, missed, and interleaved
Models were not perfect
 Cookie domain
The identical recipe can have quite different structure
For some of the recipes, there is no counterpart in the other
Impact of techniques on accuracy
 ADLs
Domain is fairly sparse, with many activities involving only
few object
 Cookie domain
Each activity model involves many more objects
Impact of techniques on compactness
Summary and Future Work
 An introduction to the idea of mining activity detection
from the web
 Future work
Perform a more comprehensive evaluation
Improving the effectiveness of mined models
Include location
Synonymous words
Synsets (collections of synonymous words) can be extracted
from WordNet