Synthetic TeammateProject
March 2009
Jerry Ball
Air Force Research Laboratory
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Synthetic Teammate Project
• Project Goal: Develop a Synthetic Teammate capable of functioning as the Air Vehicle Operator (AVO) or pilot in a 3-person simulation of a Unmanned Air Vehicle (UAV) performing reconnaissance missions
– Cognitively Plausible
• Using ACT-R
– Functional
• Large-scale
– Empirically Validated
• Not valid if it’s not functional!
few research teams
attempting to do these
at once!
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• Guiding principle: Don’t use any computational techniques which are obviously cognitively implausible
• Key Assumption: Adhering to well-established cognitive constraints may actually facilitate development by pushing development in directions that are more likely to be successful
– Short-term costs associated with adherence to cognitive constraints may ultimately yield long-term benefits
– Don’t know what you’re giving up when you adopt cognitively implausible techniques
Synthetic Teammate Project
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Synthetic Teammate Project
• Collaborative project between the Air Force Research Laboratory (AFRL) and Cognitive Engineering Research Institute (CERI)
– Applied research funds from AFRL/RHA
– Basic research funds from AFOSR
– Basic research funds from ONR
• Using the Cognitive Engineering Research on Team Tasks (CERTT) Synthetic Task Environment (STE)
– Developed with funds from AFOSR
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CERTT Synthetic Task Environment
AVO (flies UAV)PLO (takes pics) DEMPC (plans route)
Team Goal: Fly UAV
Reconnaissance
Missions
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UAV Reconnaissance Missions
• AVO, DEMPC and PLO collaborate to complete a 40 minute reconnaissance mission
• AVO must fly UAV past a sequence of waypoints which are determined by the DEMPC and communicated to the AVO as a flight plan
• Waypoints may have altitude and airspeed restrictions and have an effective radius for fly by
– Route based restrictions, waypoint type and effective radius must be communicated from DEMPC to AVO
– Photo restrictions must be communicated from PLO to AVO
• PLO must take pictures of target waypoints within the effective radius, but does not take pictures of entry and exit waypoints
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Importance of Communication
• Communication is critical to the success of reconnaissance missions
• PLO and DEMPC must communicate restrictions to AVO
• DEMPC must communicate flight plan to AVO
• When the unexpected happens—e.g. unplanned waypoint added to mission, photo missed—teammates must develop workarounds and communicate adjustments
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AVO Workstation
Instruments Warnings
Text
Chat
DEMPC to AVO: LVN is our first waypoint
AVO to INTEL: Copy
INTEL to all: OK team, mission 1, good luck.
Are there any restrictions for LVN?
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Synthetic Teammate Integration
AVO
Synthetic AVO
Teammate
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Synthetic Teammate Integration Standalone Mode
• Using an agent development framework to provide “light-weight” implementations of the DEMPC and PLO for development purposes
–Low-cognitive fidelity, scripted agents
–Eliminate need to have humans acting as DEMPC and PLO during development
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Text Chat
OutputLanguage
Comprehension
Language
Generation
Dialog
Manager
Task Behavior Model
Motor
ActionsSituation ModelVisual
Input
Text Chat
Input
System Overview
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Language
Comprehension
Language
Generation
Dialog
Manager
Task Behavior Model
Situation Model
System Overview
Text Chat
Output
Motor
ActionsVisual
Input
Text Chat
Input
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Language Comprehension
• Theory of Language Processing (Ball 2007…1991)
– Activation, selection and integration of constructions corresponding to the linguistic input
– Nearly deterministic, serial processing mechanism (integration) operating over a parallel, probabilistic (constraint-based) substrate (activation & selection)
• Theory of Linguistic Representation (Ball 2007)
– Focus on encoding of referential and relational meaning
• Implemented in a Computational Cognitive Model
– Using the ACT-R Cognitive Architecture
• Adheres to well-established Cognitive Constraints
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Cognitive Constraints
• Incremental processing – word by word
• Interactive processing – lexical, syntactic, semantic, pragmatic and task environment information used simultaneously to guide processing
– Highly context sensitive – but limited to preceding context (no access to subsequent context)
– Word recognition and part-of-speech determination integrated with higher-level syntactic, semantic and discourse processing (single pass)
• Robust processing
– Must handle ungrammatical input, incorrectly spelled words and non-sentential input
– Minimize number of “hard constraints” (e.g. whole word matching) which can lead to failure when they aren’t satisfied
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Cognitive Constraints Processing Mechanisms
• Serial, nearly deterministic (controlled) processing operating over a parallel, probabilistic (automatic) substrate
– Parallel, probabilistic substrate interactively integrates all contextual information leading to selection of the best choice given the available local context at each incremental choice point
• Soft constraints or biases
– Once a choice is made the processor proceeds serially and deterministically forward in real-time
– When a locally preferred choice turns out to be dispreferred in wider context, context sensitive context accommodation mechanism kicks in
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• The following example is from the Language Processing Model
– “no airspeed or altitude restrictions”
Language Processing in the Model
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no
“no” object specifier object referring expression
= nominal construction
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no airspeed
“airspeed” object head
Tree structures created from output of model
automatically with a tool for dynamic visualization
of ACT-R declarative memory (Heiberg, Harris & Ball 2007)
integration
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no airspeed or altitude
“airspeed or altitude” object head
Accommodation
of conjunction via
function overriding
override
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no airspeed or altitude restrictions
“airspeed or altitude” modifier“restrictions” object head
Appearance of parallel processing!
airspeed or altitude = head vs.
airspeed or altitude = mod
Accommodation
of new head via
function shift
shift
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Computational Constraints
• Processor needs to operate in near real-time to be functional
• Large-scale systems that can’t handle non-determinism efficiently (e.g. Context-Free Grammars) typically collapse under their own weight
• Deterministic processing is computationally efficient
• Probabilistic and Parallel processing—often combined with a limited “spot light”—are alternative mechanisms for dealing with non-determinism
• Parallel processing can be computationally explosive on serial hardware
– Forced to use some “hard constraints”—e.g. first letter match—in word recognition subcomponent
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Computational Constraints
• No limited domain assumption to simplify model
– CERTT text chat shows broad range of grammatical constructions and thousands of lexical items
• Relational database integrated with ACT-R to support scaling up model to a full mental lexicon
– Plan to integrate sizeable subset ( > 15,000 lexical items) of most common words in WordNet lexicon ( > 100,000 lexical items)
• Can’t ignore lexical ambiguity!
– Study underway to compare performance of model when Declarative Memory (DM) is stored in an external DB vs. internal Lisp process
• Internal Lisp process is faster for small DM, but can only handle 30% of WordNet before running out of memory!
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Start with a Domain General Language Processing System
• Contains 2000 most common words in English and 2500 words in total
• Handles a broad range of construction types
– Declarative, Imperative, Yes-No Question, Wh-Question
– Intransitive, Transitive & Ditransitive Verbs, Verbs with Clausal Complements, Predicate Nominals, Predicate Adjectives and Predicate Prepositions
– Specifier, Head, Complement, Pre- and Post-Head Modifier
– Conjunctions of numerous functional categories
– Relative Clauses, Wh-Clauses, Infinitive, -ing, -en & Bare Verb Clauses
– Long-distance dependencies
– Passive constructions
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Start with a Domain General Language Processing System
• Representations are in the spirit of the “Simpler Syntax” of Culicover & Jackendoff (2005) except that there are no purely syntactic representations
Semantic Features
Trace bound
to subject
Functional
Categories
Referring Expression
Predicates
He is eager to please.
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Extend to Handle Scripted Comm
• AVO: DEMPC, please let me know the first waypoint!
• DEMPC: The first waypoint is LVN. It’s an entry point. There are no airspeed or altitude restrictions. The effective radius is 2.5 miles.
• AVO: PLO, I’m heading towards LVN.
• DEMPC: We’re within the effective radius so go to the second waypoint.
• AVO: Are there any altitude or airspeed restrictions for the second waypoint?
• DEMPC: The second waypoint is H-AREA. It’s a target. The airspeed restriction is between 50 and 200 knots. There is no altitude restriction. The effective radius is 5 miles.
• PLO: AVO, please keep the altitude over 3000 feet for the photo!
• PLO: I have a good photo of H-AREA.
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Scripted Comm
• Full sentences
• Correct spelling
• Explicit discourse acts
• Still lots of variability
– Declarative sentences
– Imperative sentences
– Questions
– Conjunctions
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Extend to Handle Text Chat for a 40 Minute Mission – without editing!
• PLO to AVO: avo-don't ever proceed from a target if i haven't taken the picture
• AVO to PLO: ok -- keep me in the loop!
• INTEL to all: ok team, mission 2
• PLO to AVO: effective radiu
• PLO to AVO: avo i need to be below 3000
• AVO to PLO: copy, will 2000 do?
• DEMPC to AVO: LVN is our 1st entry point with a radius of 2.5
• AVO to PLO: speed?
• AVO to DEMPC, PLO: 1 mile out/ 30 seconds
• PLO to AVO: i don't have a speed for lvn so go faster
• AVO to DEMPC, PLO: speed 340
• PLO to AVO: avo i'll need to be above 3000 for h area
• AVO to PLO: above 3000 copy -- can we proceed to h-area yet?
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Extend to Handle Text Chat for a 40 Minute Mission – without editing!
• PLO to AVO: lets get out of effective zone
• DEMPC to AVO: Speed=50-200, Altitude=500-2000
• AVO to DEMPC, PLO: wait -- my flight plan changed -- are we going to Z1?
• PLO to AVO: can yougo faster yet or is it stll 200
• DEMPC to AVO: no speed or alt. restrictions
• PLO to AVO: avo i need to be above 3000 for s ste- go there when you think it would be most effective
• PLO to AVO: avo 3000
• DEMPC to AVO: YES to S-StE=Target
• PLO to AVO: `avo get back within 5 miles of s ste
• PLO to AVO: aavo dont slow down
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Handle Communication with Unscripted Human DEMPC and PLO
• Language varies significantly from team to team
– Can’t predict vocabulary requirements in advance
• Teams adapt particular ways of communicating which can’t be predicted in advance
– Text becomes more cryptic as mission continues
• Discourse acts are often implicit
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Word Recognition Subcomponent
• Word recognition subcomponent largely compatible with the E-Z Reader model of reading (cf. Reichle, Warren & McConnell 2009) with extensions to support higher-level language processing
• Perceptual window used for low-level processing of linguistic input
– Model can “see” space delimited “word” in focus of attention
– Model can “see” up to first 3 letters of word in right periphery following space
• Retrieved word is verified against actual input
– Consistent with Activation-Verification model of Word Recognition (Paap et al. 1982)
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Word Recognition
• Word recognition is an interaction between low-level perceptual and higher-level cognitive processing
• Perceptually identified letters, trigrams and space delimited “words” spread activation to words (and multi-word units) in DM
• Most-highly activated word or multi-word unit consistent with retrieval template is retrieved
– Need not be a space delimited “word”
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Generating Linguistic Representations
• Incremental, interactive generation of linguistic representations which encode referential and relational meaning
Referring Expressions
Relations
He is eager to please.
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Mapping into the Situation Model
• Referring expressions in the linguistic representation get mapped to objects and situations in the situation model
• Indefinite object referring expression typically introduces a new object into the situation model
• Definite object referring expression typically identifies and existing object either in the situation model or salient in the context
• Situation referring expressions typically introduce a new relation into the situation
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Language
Comprehension
Language
Generation
Dialog
Manager
Situation Model
System Overview
Text Chat
Output
Motor
ActionsVisual
Input
Text Chat
Input
Task Behavior Model
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Centrality of Situation Model
DomainKnowledge
Task Behavior
World KnowledgeSituation
Model
Language OutputLanguage Input
Language Knowledge
Task Input
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Situation Model
• Situation Model (Zwann & Radvansky, 1998)
– Spatial-Imaginal (and Temporal) representation of the objects and situations described by linguistic expressions and encoded directly from the environment
• Non-propositional (at least in part)
• Non-textual
• No available computational implementations
– Provides grounding for linguistic representations
– Integrates task environment and linguistic information
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Abstract Concepts vs. Perceptually Grounded Language
“pilot”“pilot” “pilot”
PILOT
Real World Mental Box Mental BoxReal World
perception Language
of Thought
The Prevailing View An Emerging View
gro
un
din
g
perceptionImplicit(Abstract)
Explicit(Perceptual)
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Abstract Concepts vs. Perceptually Grounded Language
“pilot”“pilot” “pilot”
PILOT
Real World Mental Box Mental BoxReal World
perception Language
of Thought
The Prevailing View An Emerging View
gro
un
din
g
perceptionImplicit(Abstract)
Explicit(Perceptual)
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Situation Model
• Propositional Content
– Planning to use Hobbs’ theory of “ontological promiscuity” and his well-developed logical notation (translated into ACT-R chunks) to represent propositional content
• The logical notation should be as close to English as possible
• The logical notation should be syntactically simple to support inferencing
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Situation Model
• Spatial Content
– Planning to use Scott Douglass’ spatial module extension to ACT-R which implements a matrix-like representation of spatial information
• Discourse Content
– Working on identification and representation of Discourse Acts which are often only implied in linguistic input
• “I need to be above 3000 feet for the photo”
–This is a request to increase the altitude of the UAV (human is not actually in UAV)
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Empirical Validation
• Experiment conducted with human subjects in conditions using 1) spoken language and 2) text chat to provide data for model development
– AVO station moved into separate room so DEMPC and PLO don’t see AVO
– Text chat condition showed team performance effect similar to spoken language condition
• Goal is to conduct an experiment with Synthetic AVO Teammate interacting with human DEMPC and PLO
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Questions?