the common sense dj
DESCRIPTION
The Common Sense DJ. Arnan (Roger) Sipitakiat Carla Gomez Monroy Joan Morris DiMicco Luke Ouko. MAS.964 Final Project December 2002. Project Goals. Create a reasoning system that: Utilizes common sense knowledge from Thought Treasure. Adapts suggestions to the current - PowerPoint PPT PresentationTRANSCRIPT
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The Common Sense DJ
Arnan (Roger) SipitakiatCarla Gomez MonroyJoan Morris DiMiccoLuke Ouko MAS.964
Final ProjectDecember 2002
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Project Goals
Create a reasoning system that:Utilizes common sense knowledge fromThought Treasure.Adapts suggestions to the current environmental context.Observes reactions to suggestions to learn new or corrective CS.
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Overview of theCommon Sense DJ
CSDJ application suggests songs to play through common sense Thought Treasure = knowledge source Thought Treasure reasons about what song type to play Java interface collects feedback from real-life DJ and suggests songs Camera senses dancing, allows feedback to Thought Treasure
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CSDJ Architecture
Thought Treasure
Serve API
ProverDB
JAVA Interface
JAVA APICameraTracker
ProtocolProtocol
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Thought Treasure
Hierarchical knowledge storage
structurePrimary features: NLP, Spatial
representation, planning.Provides simple rule-based reasoning
engine
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Music Categorization
By Culture By Continent: Asian, European, etc. By Country: American, Mexican, Thai, etc.
By Age Teens, 20s, 30s, 40s, 50s, 60s
By Profession Classic (Conservative), Artistic (Liberal)
By Domicile Rural, Urban
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Preliminary Reasoning
59 countries in Asia x 5 music eras x 18
music genres5,310 possibilitiesWhen all attributes are known, rules can
filter this down to 3-10 possibilities.
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Preliminary Reasoning(examples)A liberated crowd in their 20s from an urban part of Mexico probably likes: Mexican salsa, electro, alternative rock.Conservative Americans in their 50s from an urban city probably likes: rock music from the 60s and 70s (Elvis, the Beatles)
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Need for further reasoning
Too much data and conflicting data
when some attributes are missing.
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Further Reasoning:Prover CriticsAnalyzes the preliminary output and
detect situations when the output is
useless or self-conflicts.Then, it goes through a set of scenarios
to improve the output.
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Examples of Scenarios
While Culture is unknown. It is better to
play cross-culture music than to guess.If profession or domicile is unknown
then try to guess.If all attributes are known but people are
not dancing then: Try to increase the tempo. Some attributes may not be true anymore.
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Further Reasoning:The Learning CriticA tracking system provides feedback
data upon which the system reflects its
decision.new rules are added when feedback
differs from current rules.
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Conclusions about TT
Chosen because of the built-in structure
and reasoningStructure restrictive, not enough
knowledgeWith Mueller’s help, extended TT,
extended the Java API, and fixed bugs
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Technical Implementation
Thought Treasure
ServeAPI
Func
Func
Func
DB
Facts
Func
Func
ProverRules
Cities
JAVA Interface
JAVA API
Func
Func
Func
Camera Tracker
Camera
Protocol
Pro
toco
l
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Camera SensorHouse_n technologyDetects number of people in view and
number dancingSends feedback to Common Sense DJ
for learning
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Demo!
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Conclusions
Built application using TT’s knowledge
and reasoning powerThe CSDJ builds suggested play list
based on dance club’s appearanceSystem refines TT’s CS knowledge
based on crowd’s reaction to songs
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Thanks!
Arnan (Roger) Sipitakiat
Carla Gomez Monroy
Joan Morris DiMicco
Luke Ouko