topic 12: level 3
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TOPIC 12: LEVEL 3
David L. Hall
TOPIC OBJECTIVES
Introduce the JDL Level 3 Process Describe modeling, prediction and analysis
techniques for Level 3 Indentify limitations and issues for level 3
processing
COMMENTS ON THIS LECTURE
The lectures on level-2 and level-3 could easily be merged (although it would be a very long lecture), since the methods described for level-3 are a continuation of the automated reasoning methods introduced in the level-2
The examples presented have a DoD/military flavor since an enormous amount of research has been funded in this area – with extensive developments
These examples and methods are easily extendible to non-military applications such as environmental monitoring, public health, disaster relief and other areas
LEVEL 3 PROCESSING (CONSEQUENCE
REFINEMENT)
SITUATION AND THREAT ASSESSMENT FUNCTIONS
a prioriIntelligence Preparation
Technical Doctrinal
Databases
Threat (RISK)
Analysis
Situation Abstractions
Situation Assessment
N
2
1
. . .
Command and
Control Decision-Making
Level 1 Data Fusion
Products
• Construct Representa- tions of Data
• Interpret and Express the Environment
– Objects– Groups– Events– Activities
• Predict Future Courses of Action• Three Perspectives
– White– Blue– Red
Level 2,3 Data Fusion
Products
• Multiple Possible Explanations of Situation and Threat
• Planning• Expected Outcomes of Blue COAs
LEVEL THREE PROCESSING:THREAT REFINEMENT
LEVEL THREE PROCESSING
THREAT REFINEMENTTHREAT REFINEMENT
ESTIMATE/AGGREGATE
FORCE CAPABILITIES (RED/BLUE)
PREDICT ENEMY INTENT
IDENTIFYTHREAT
OPPORTUNITIES
ESTIMATE IMPLICATIONS
• Force vulnerabilities• Timing of critical events• Threat system priorities• Friendly system opportunities
MULTI-PERSPECTIVE ASSESSMENT
• Offensive/Defensive
COMMANDER’S DECISION-
MAKING METHODOLOGY
SENIOR COMMANDER’S
CONCEPT
OWN TROOP’S MISSION
INTERMEDITE MISSIONS
Mission Tree(Objectives
to be hit)
Particular Missions
Analysis of Situation
Concept of Combat Operations
Tactical Missions of Sub-units of Troop Branches
Troop Coordination Procedure
Measures for Political Work Combat Operations Support, and Organization
of Command and Control
OPTIONSELEMENTDECISION
Analysis of Mission
Selection and Formulation of Best Decision Option
ENEMY
OWN TROOPS
ADJACENT UNITS
TERRAIN
HYDROMETEOROLOGICAL CONDITIONS, TIME OF YEAR
RADIATION SITUATION
ECONOMIC CONDITION OF COMBAT OPERATIONS
AREA & SOCIOPOLITCAL MAKE-UP OF POPULATION
SITUATION ELEMENTS
NOTIONAL ENEMY COURSE OF ACTION DISPLAY*
Mountain 1
Road 2
Bridge 2
River 2
Bridge 1
Road 1
Large Barrier
Marsh 1
Forest 1
Lake 1
River 1
Extended Barrier
* Antony, R., Principles of Data Fusion Automation, Artech House, Inc., Norwood, MA, 1995, p. 92.
Richard Antony discusses the “Intelligent Preparation of the Battlefield” concept and related situation and decision-support displays; such concepts are often used in a wide variety of areas including business continuity planning, preparation for disasters & disaster relief; and many large-scale operations
INTELLIGENCE PREPARATION OF THE BATTLEFIELD PROCESS
INTELLIGENCEDATA
IDENTIFIEDENEMY
HOLDINGS
ENEMYDOCTRINE
WEATHERANALYSIS
PERCEIVEDENEMY
SITUATION
TERRAIN ANALYSIS
LIKELYENEMYACTION
AREASOF
INTEREST
COMMANDERDECISIONS
COLLECTIONMANAGEMENT
THREAT EVALUATION THREAT INTEGRATION
Expected Enemy
Reactions to Decisions
ASSESSING THE THREAT/CONSEQUENCES
• WHITE VIEW OF THE BATTLEFIELD ENVIRONMENT• RED VIEW OF THE ENEMY WAR PLAN• BLUE VIEW OF THE FRIENDLY FORCE MISSIONS
GENERAL NOTION OF THE THREAT MODEL:GENERAL NOTION OF THE THREAT MODEL:
WHITE VIEW BLUE VIEW RED VIEW
• Effects of the EnvironmentEffects of the Environment– Weather– Terrain– Political treaties– Communication nets
• Operational ArtOperational Art– Objectives– Offensive– Economy of force– Maneuver– Unity of command– Security– Surprise– Simplicity
• Enemy Battle PlansEnemy Battle Plans– Why– When– Where– Force structure– Objectives– Time table– Options– Tactics– Doctrine
Note: the concept of “shifting” perspectives is a valuable tool in addressing nearly any situation and it’s consequences; what am I planning to do or want to happen (blue view); how might others react to my plans and activities (red view), and how the environment affect both me and others (white view)?
KNOWLEDGE REPRESENTATION
Physical and mathematical models Equations Neural Nets
Language constructs Ontology/Taxonomy Logical constructs (e.g. predicate logic) Examples, Stories and Cases
Analogical models Graphs Trees Special notations (chemical symbols, musical
notes) Diagrams Cognitive Maps Etc
MAJOR REASONING APPROACHES
Knowledge representation Rules Frames Scripts Semantic nets Parametric Templates Analogical methods
Uncertainty representation Confidence factors Probability Dempster-Shafer evidential intervals Fuzzy membership functions Etc.
Reasoning methods & architectures
Implicit methods Neural nets Cluster algorithms
Pattern templates Templating methods Case-based reasoning
Process reasoning Script interpreters Plan-based reasoning
Deductive methods Decision-trees Bayesian belief nets D-S belief nets
Hybrid architectures Agent-based methods Blackboard systems Hybrid symbolic/numerical systems
PATTERN TEMPLATES
Logical Templating Methods Case-Based Reasoning
LOGICAL TEMPLATES
Based on a concept similar to grading tests or papers using a template (score sheet) to quickly determine the number of correct answers
Logical templates can be created including parametric relations, causal factors, sub-entities, etc. to characterize a complex entity, activity or event
Logical template methods are an extension of decision-trees and pattern recognition
Test paper Name ______1) 2)
Answer Sheet
1)2)
Student answers are matched against correct answer sheet to see how many the student got correct
INTELLIGENCE PREPARATION OF THE
BATTLEFIELD TEMPLATESTemplate Description Purpose When Prepared
Doctrinal Enemy doctrinal deployment forvarious types of operations withoutconstraints imposed by weatherand terrain. Composition,formations, frontages, depths,equipment numbers and ratios,and high value targets (HVT) aretypes of information displayed.
Provides the basis for integratingenemy doctrine with terrain andweather data.
Threat Evaluation
Situation Depicts how the enemy mightdeploy and operate within theconstraints imposed by theweather and terrain.
Used to identify critical enemyactivities and locations. Provides abasis for situation and targetdevelopment and HVT analysis.
Threat Integration
Event Depicts locations where criticalevents and activities are expectedto occur and where critical targetswill appear.
Used to predict time-related eventswithin critical areas. Provides abasis for collection operations,predicting enemy intentions, andlocating and tracking HVT.
Threat
DecisionSupport
Depicts decision points and targetareas on interest keyed tosignificant events and activities.The intelligence estimate is ingraphic form.
Used to provide a guide as to whentactical decisions are requiredrelative to battlefield events.
Threat Integration
TEMPLATE PROCESSING
FLOW
B
START
STOP
Receive Triggering Information
Receive Candidate Template
Receive Related Events from Database
Perform Logic Checks
Ending Processes
Make Identification Declaration
Compute MOC
A
A
Make Ambiguity Declaration
YES
NO
YES NO
YES
NO
YES
PassNecessary
Test
PassSufficiency
Test
MOC > TR
MOC > TA
BMore
Templates?
NO
NO
YES
Notes:Notes:
TA = Acceptance Threshold
TR = Rejection Threshold
MOC = Measure of Correlation
SPEEDING TICKET TEMPLATE EXAMPLE
Threat elements Moving violations
Speeding ticket DUI Reckless driving Failure to stop at stop sign
or stoplight Non-moving
violations Illegal parking Failure to have vehicle
inspected Other
Speeding Ticket Threat Template
White Conditions• Speed limit• Visibility• Posted speed limit• Location wrt
known speed traps
• etc
Blue Conditions• Own car speed• Condition of driver• Appearance of driver• Gender of driver• Color of vehicle• etc
Red Conditions• RWR indicator• Visible enemy• COMINT externals• COMINT internals• etc.
Logical relations• If RWR and own car speed >(1.2* speed limit) threat• Etc
WHAT IS CASE-BASED REASONING?
Cases are descriptions of situations and the actions taken to respond to them
Case-based reasoning is an approach to building knowledge systems that: Bases reasoning on retrieval of cases that are similar to the
current situation Supports learning from experience
Reference: J. Dannenhoffer, Case-Based Reasoning, presented to the AIAAA AI Technical Committee, January 1992.
THE CASE-BASED REASONING PROCESS
Reference: J. Dannenhoffer, Case-Based Reasoning, presented to the AIAAA AI Technical Committee, January 1992.
ACCEPT NEW CASE
RETRIEVE RELEVANT CASES
SELECT MOST RELEVANT CASE(S)
CONSTRUCT SOLUTION OR INTERPRETATION OF NEW CASE
VALIDATE SOLUTION/INTERPRETATION
UPDATE MEMORY WITH NEW CASE
PROCESS REASONING
Script Interpreters Rule-bases systems Expert Systems
Plan-based Reasoning
GENERAL CHARACTERISTICS OF RULE-BASED SYSTEMS (PROCESS
REASONING)Application Domain:Application Domain:
Approach:Approach:
Development:Development:
Evaluation:Evaluation:
• Specific fairly narrow real-world problems (poor/missing data)
• Heuristic, rule-based search strategies in general plus facts and computation methods• Knowledge engineering/knowledge representation• Control: data-driven or goal directed• Software: LISP, PROLOG or other script-like
language
• Development support system• Incremental, evolutionary development process
• No absolutes -- experts are evaluators
In the early heyday of AI research (1980s & early 1990s) these types of reasoning were termed “expert” systems
BASIC STRUCTURE OF AN EXPERT SYSTEM
USERUSER
MAN-MACHINE INTERFACE
CONTROL STRUCTURE(RULE INTERPRETER/INTERFACE
ENGINE)
KNOWLEDGE BASE
• Heurtistics• Facts• Algorithms
GLOBAL DATABASE(Dynamic System
Status)
SystemInput
CONCEPTUAL INFERENCE CYCLE
KnowledgeBase (KB)
DynamicData
Search KB
AnyRules
?
Select Rule
Done ?
Quit
Quit
YESYES
NONO
NONO
Fire/Execute Rule
• Update Dynamic Data• Execute Sub-Routine• Request Input Data• Etc.
KNOWLEDGE ENGINEERING
MILITARY/DOMAIN EXPERTMILITARY/DOMAIN EXPERT
• Military Organization/Protocols• Rules of Engagement• Military Doctrine• Military Equipment Characteristics
– Weapons– Electronics
• Communications
KNOWLEDGE BASEKNOWLEDGE BASE
• Scenario• Rule Base• Tree Constructs• Database Design• Facts/Algorithms
SOFTWARE ENGINEERSOFTWARE ENGINEER
• Development Support System• Soft Programming• Software Architecture• Computer Environment• Numerical Techniques
PLANNING/GOAL DECOMPOSITION
Planning provides another effective way to represent knowledge including timelines, roles and responsibilities, hierarchies of plans, causality, etc.
Planning analogies have been used effectively for automated reasoning including course of action analysis tools, impact analysis, decision trees, hypothesis evaluation, gaming methods and more recently team-based intelligent agents
Plan A Plan B
GOAL DECOMPOSITION
ACCOMPLISH MISSION
ATTACK TARGET SURVIVE THREAT
Detect Candidate
Target
Evaluate Target
Determine Attack Tactic
Identify Threat
Monitor Threat
Determine Threat Tactic
Specify Target
Target of Opportunity
Acquire Target
Select Weapon
Select Attack Profile
Revise Plan
Estimate Range
Bearing
Infer Status
Intention
Avoid Threat
Suppress Threat
Revise Plan
CONCEPT OF GOAL/PLAN HIERARCHY
Known Target Locations
Defend againstTarget
MILITARY GOALS
Reconnaissance CoordinationBlockage
Feint
Surveillance StrikeTankOperations
Damage Assessment
CounterMeasures
SINGLE-AGENT PLAN LIBRARY
MULTI-TARGET MISSION TEMPLATE LIBRARY
Monitor Mission Defend Mission
• • •
• • •
• • •
Destroy Target
Attack Mission
DEDUCTIVE METHODS
Decision Trees Bayesian Belief Nets
Belief networks Bayesian networks Causality nets, etc.
Dempster-Shafer Belief Nets
BAYESIAN BELIEF NETS Representation of relationships or causality via Bayesian probability
(publicized by Judea Pearl in 1988) Knowledge contained in
Directional acyclic graph Nodes represent variables Links express (parent/child) relationships (e.g. causal
relationships) Each node has a conditional probability relation specified
Knowledge propagation via Bayesian chain rule Network as a whole represents the joint probability distribution Note: Bayesian Networks also called Markov Chain
See for example the tutorial at: http://www.cs.ubc.ca/~murphyk/Bayes/bayes.html
BAYESIAN BELIEF NETS
A
B C
D
E
Example of a directed acyclic graph
BAYES NET FOR TARGET IDENTIFICATION
Target, No Target
Land Sea Air
Target Type(Tgt1, Tgt2, …, Non-Tgt)
Target Activity(launch, hide, reload, move)
Target Dimension
CommEquipment
RadarType
CommActivity
Radar Activity
Length(IMINT)
Width(IMINT)
Frequency(COMINT)
Duration(COMINT)
PRI(ELINT)
Frequency(ELINT)
• Evidence can be injected into any node in the form of a likelihood function This increase propagates to the parent nodes and the children nodes Propagation continues until all nodes have been updated• The sum of probabilities in a set of children equals that of the parent
Example provided by KC Chang via M. Liggins
HYBRID METHODS
Blackboard Systems Agent-based Architectures Hybrid Symbolic/Numeric Systems
EXAMPLE: BLACKBOARD ARCHITECTURE CONCEPT
S
HA
RE
D M
EM
OR
YS
HA
RE
D M
EM
OR
Y
PROBLEM DOMAINPARTITION N
PARTITION 3
PARTITION 2
PARTITION 1
KA
KA
KA
KA
KB
KB
KB
KB
CONTROL STRUCTURE CONTROL STRUCTURE
EXTERNAL INTERFACEEXTERNAL INTERFACEHUMAN COMPUTER INTERFACEHUMAN COMPUTER INTERFACE
KA = KNOWLEDGE AGENTKB = KNOWLEDGE BASE
SUMMARY OF AGENT ATTRIBUTES
Bradshaw (1997) lists the following as possible agent attributes:•Reactivity. the ability to selectively sense and act•Situatedness. being in continuous interaction with a dynamic environment, able to perceive features of the environment important to them, and effect changes to the environment.•Autonomy. goal-directedness, proactive and self-starting behavior•Temporal continuity. persistence of identity and state over long periods of time•Inferential capability. can act on abstract task specifications using prior knowledge of general goals and preferred methods to achieve flexibility, goes beyond the information given, and may have explicit models of self, user situation, and/or other agents•Adaptivity. being able to learn and improve with experience•Mobility. being able to migrate in a self-directed way from one host platform to another across a network
SUMMARY OF AGENT ATTRIBUTES (CONT.)
• Social ability - the ability to interact with other agents(and possibly humans) via some kind of agent-communication language, and perhaps cooperate with others.
• Knowledge-level'' communication ability. the ability to communicate with persons and other agents with language more resembling human-like “speech acts” than typical symbol-level program-to-program protocols
• Collaborative behavior. can work in concert with other agents to achieve a common goal
Wooldridge and Jennings [4] add the following as possible agent attributes:
• veracity - an agent will not knowingly communicate false information
• benevolence - agents do not have conflicting goals, and that every agent will therefore always try to do what is asked of it, and
• rationality - an agent will act in order to achieve its goals, and will not act in such a way as to prevent its goals being achieved — at least insofar as its beliefs permit
INTELLIGENT AGENT AUTOMATED REASONING
En
vironm
ent
Sensors
Effectors
Agents perception of world
What world will be like if I do actions
A, B, F ….
Internal Model of World State
How the world is changing
How actions change world
Action to performGoalsE
nviron
men
t
Sensors
Effectors
Agents perception of world
What world will be like if I do actions
A, B, F ….
Internal Model of World State
How the world is changing
How actions change world
Action to performGoals
Agent Characteristics
•Wish agent to be pro-active
•Agent maintains a list of one or more goals
•A goal is a description of a desirable situation (state of the world)
•Actions are chosen so as to achieve the goals
•Deliberative – agent needs to reason about the actions to take to achieve goals
•Goal achievement may involve long sequences of actions – may involve extensive search and planning
Goal-based reactive agents can be developed to emulate human-like behavior for information search and understanding
• Multi-agent logic language for encoding teamwork (MALLET)
Act onInfo Needs
IdentifyInfo Needs
(DIARG)
InformationNeeds
Responsibilities(Petri Nets)
TeamKnowledge(MALLET)
ResponsibilitySelection
Belief
DomainKnowledge
(JARE)
BeliefUpdate
Information
See research by Dr. John Yen at http://agentlab.psu.edu/
Example of Team-Based Intelligent Agents to Support Data Fusion
Information Fusion 2+
Information Fusion 1
Information Fusion 1
Information Fusion 1
Team DecisionContext
Computational SMMContext
Shared MentalModel
Information Fusion 2+
Information Fusion 1
Information Fusion 1
Information Fusion 1
Team DecisionContext
Computational SMMContext
Shared MentalModel
TOPIC 12 ASSIGNMENTS
Preview the on-line topic 12 materials Read Wark and Lambert chapter 11 referenced
above Read chapter 2 in Mlidinow (2008)
DATA FUSION TIP OF THE WEEK
Level 3 processing is ultimately about consequence prediction – assisting a user/analyst in determining how the current situation may evolve (i.e., alternate hypothetical futures), how these alternative futures may affect the current situation, how to identify potential decisions and how to evaluate the consequences of alternative decisions. We need to seek a balance between providing insight for the analyst/decision-maker without inducing “analysis paralysis”.
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