OntologiesReasoningComponentsAgentsSimulations

Model-Driven Engineering Model-Driven Engineering of Intelligent Agentsof Intelligent Agents

Jacques Robin

OutlineOutline

Model-driven engineering Model-driven software components Model-driven components for intelligent agents The ORCAS project: automated reasoning components The IATE project: agent environment product line

Model-Driven EngineeringModel-Driven Engineering

AbstractionScale

ExecutabilityScale

UML + NL Requirements

UML Model

SourceCode

VirtualMachine

Code

BinaryCode

Current Standard Practice:Object-Oriented Development

Model-Driven EngineeringModel-Driven Engineering

AbstractionScale

ExecutabilityScale

VirtualMachine

Code

BinaryCode

UML/OCLRequirements

UML/OCL Specification

PIM

UML/OCL Realization

PIM

ProfiledUML/OCL

Realization PSM

SourceCode

Today’s MDD:Extreme Modeling

Model-Driven EngineeringModel-Driven Engineering

AbstractionScale

ExecutabilityScale

VirtualMachine

Code

BinaryCode

UML/OCLRequirements

UML/OCL Specification

PIM

SourceCode

ProfiledUML/OCL

Realization PSM

UML/OCL Realization

PIM

ModelTranslation

Patterns

ModelRefinement

Patterns

Tomorrow’s MDD:UML Programming

Model-Driven Software Components: Model-Driven Software Components: the KobrA methodthe KobrA method

Comprehensive, prescriptive methodology Coherently integrates most software reuse concepts

+ Aspect-Oriented Development+ Built-in Component Contract Test Generator

Model-Driven Software Components: Model-Driven Software Components: the KobrA methodthe KobrA method

Full Life-CycleComponents

Intra and inter-diagram consistency and completeness rules

Recursive decomposition of components in sub-components

Realization models fully refined, code generation through pure translation

IATE

ORCAS

Agent 1 Component

Component-BasedComponent-BasedMulti-Agent Simulation EngineeringMulti-Agent Simulation Engineering

EnvironmentSimulator

Component

Agent N Component

DomainKnowledge

Base

Inference Engine Component Inference Engine Component

DomainKnowledge

Base

InferenceEngine

ComponentDomain

KnowledgeBase

InferenceEngine

Component

DomainIndependentKnowledge

Base

InferenceEngine

Component

DomainIndependentKnowledge

Base

Perceptions, Actions

Perceptions, Actions

SimulationIndependent

DomainModel

EnvironmentCharacteristics’

Model

KnowledgeBase

Generator

ORCAS motivation: why automated ORCAS motivation: why automated reasoning still so rarely used today?reasoning still so rarely used today?

Too costly to develop AR services from scratch for specific applications: AR services are large, conceptually very complex software They require hard to find software engineers with strong AI

background

Too costly to integrate available AR software in larger systems: Rarely based on mainstream IT standards (e.g., exotic Prolog

extensions) Rarely comes with adequate middleware, (e.g., only API to C) Single software rarely provides all AR services required by

application

Too costly to customize or extend available AR software for specific applications: Never comes with models or test suite (at best with source code) Often comes without reasoning explanation GUI Never integrated with standard CASE tool or IDE

ChallengingTestbed

ORCAS: long-term goal ORCAS: long-term goal and methodologyand methodology

Automated Reasoning:Automated Reasoning:• Adaptive Constraint Handling Rules

• Transaction Frame Logic

Reusable Reasoning ComponentReusable Reasoning Componentss::• w/ Full Artw/ Full Artiifact Suite: UML Requirement, Analysis and Design Models,fact Suite: UML Requirement, Analysis and Design Models, AspectJ Code, AspectJ Code, Test Suite Test Suite

• For Deduction, Abduction, Induction, Belief Revision, Constraint Solving, Optimization

• In Logical, Probabilistic, Decision-Theoretic Settings

• w/ Object-Oriented, First-Order Rewrite Rules Automatically Generated from UML modelsAutomatically Generated from UML models

UsabilityExtensibility

Software Engineering:Software Engineering:• Model-Driven Development

• Component-Based Development

• Aspect-Oriented Development

• Built-in Contract Testing

KobrA MethodKobrA Method:• Customized for Automated Reasoning• Upgraded to Latest OMG Standards

KnowledgeLevel

ImplementationLevel

DeploymentLevel

ORCAS: 4-layer ORCAS: 4-layer model-driven developmentmodel-driven development

FormalizationLevel

CHORDBase

KobrAUML/OCL/QVT

Model

AspectJ/Javaor Aspect#/C#Source Code

.Jar or DLLComponent

AspectJ Weaver& Java Compiler

or Aspect# Weaver& C# Compiler

CHORD toAspectJ/Java

or Aspect#/C#Compiler

UML/OCL/QVT to CHORDCompiler

Applies to both: Domain-dependent application knowledge base development Domain-independent inference engine development

CHORD: MotivationCHORD: Motivation

ORCAS’s middle-tier knowledge representation formalism: Object-Oriented Rule-Based Constraint Programming

Why constraint programming? Showed to subsume most other reasoning tasks:

Deduction, Abduction [Abdennadher & Schutz], Belief Revision [Wolf], and Optimization [Dechter]

In Logical [Abdennadher & Schutz], Probabilistic [Angelopoulos] and Decision-Theoretic [Dechter] settings

Why rule-based? Rules easier to specify and revise than algorithms for non-programmers

domain experts Declarative semantics in formal logic allows model checking Declarativeness allows fast prototyping (i.e., agile development)

Why object-oriented? Easier compilation from object-oriented UML/OCL upper-tier Easier compilation to object-oriented Java, C# or C++ lower-tier Reuse through inheritance

Integrating two versatile formal Integrating two versatile formal knowledge representation languagesknowledge representation languages

CHRDConstraint Handling Rules w/ Disjunction

Subsumes:• Prolog, Deductive DB• Production Systems• Term Rewriting Systems• Concurrent Constraint Logic Programs• Truth-Maintenance Systems• Abductive Logic Programs• (Semantic Web) Description Logics

Available Implementations: Prolog, Java

Logic-Based Formal Semantics

Transaction Frame LogicSubsumes:

• Prolog, Deductive DB• Tabled Logic Programs• High-Order Logic Programs• Object-Oriented Logic Programs• Transactional, Relational DB• Object-Oriented, Object-Relational DB• Semi-Structured XML DB• Semantic Web RDF DB

Available Implementations:

• Prolog, Java, C++

Logic-Based Formal Semantics

CHORD = CHRD Rules + Frame Logic Objects = CHRD Rules + Frame Logic Objects

CHRDCHRD in a nutshellin a nutshell

Knowledge representation language for theorem proving integrating term rewriting, forward chaining and backtracking

Based on subset of first-order predicate logic Distinguishes between two classes of predicate symbols:

Locally defined predicates (in subject CHR component) Externally defined predicates (in server CHR components)

Extends CHR that was created for declarative programming of constraint solvers

CHRD program: logical conjunction of rewrite rules (clauses) Two types of rules:

Constraint simplification rules (logical equivalences, rewrite rules) Constraint propagation rules (logical implications, production rules)

CHRD: simplification rulesCHRD: simplification rules

head guard | body l10(…,U,…) … ln0

(…,V,…) e1(…,W,…) em(…,X,…) |(p1

1(…,Yi,…) … p1p(…, Yj,…)) … (pk

1(…,Yk,…) … pkq(…,Yl,

…)) Example:

min(X,Y,Z) int(Z) int(Y) int(Y) X Y | Z = X. Logical declarative semantics:

…,U,…,V,…,W,…X,… (e1(…,W,…) em(…,X,…)) (l10(…,U, …) … ln0

(…,V, …) ( …,Yi,…,Yj,… p1

1(…,Yi,…) … p1p(…, Yj,…))

… …,Yk,… Yj,… pk

1(…,Yk,…) … pkq(…,Yl,…))))

Operational semantics: If external solver component query returns true on guard

then substitutesubstitute constraints matching head in local constraint store with constraints in rule body modulo variable substitution

Otherwise try other rules

CHRD: propagation rulesCHRD: propagation rules

head guard | body l10(…,U,…) … ln0

(…,V,…) e1(…,W,…) em(…,X,…) |(p1

1(…,Yi,…) … p1p(…, Yj,…)) … (pk

1(…,Yk,…) … pkq(…,Yl,

…)) Example:

min(X,Y,Z) int(Z) int(Y) int(Y) | Z X Z X. Logical declarative semantics:

…,U,…,V,…,W,…X,… (e1(…,W,…) em(…,X,…)) (l10(…,U, …) … ln0

(…,V, …) ( …,Yi,…,Yj,… p1

1(…,Yi,…) … p1p(…, Yj,…))

… …,Yk,… Yj,… pk

1(…,Yk,…) … pkq(…,Yl,…))))

Operational semantics: If external solver component query returns true on guard

then addadd to local constraint store constraints in rule body modulo variable substitution needed to match rule head with constraints already in the store

Otherwise try other rules

Extends Prolog with object-oriented terms: Class definition (hierarchy + signature):

c1::c2[..., ai *=> ti ,..., mj(tj1,...,tjk) *=>> til ,...]. e.g.: person[name => string, children(person) *=>> person]. man::person[sex -> male].

Transaction Frame Logic: Transaction Frame Logic: object-oriented termsobject-oriented terms

• c1 subclass of c2

• ti type of attribute ai of class c1

• til return type of method mj of c1

• tj1,...,tjk parameter types of method mj of c1

• o object instance of class of c• vi value of attribute ai of o• vil return value of method mj of o wheninvoked with input parameter values vj1,...,vjk

FLOOM:Frame Logic

Object-OrientedMolecules Object creation:

o:c[..., ai -> vi ,..., mj(vj1,...,vjk) -> vil ,...]. e.g.: al:man[name -> “Al”, children(ann) -> dan].

Transaction Frame Logic: high-order Transaction Frame Logic: high-order OO syntax and update predicatesOO syntax and update predicates

Allows logical variable in any position: ?- person[name -> N, children(P1) *->> P2]. ?- Object:Class[Attibute -> john, Method(Parameter) *->> terry].

Allows path expressions, similar to OCL navigation expressions: ?- person.name = N, person.children(P1) = P2.

Extends Prolog w/ new, backtrackable update predicates: P:person[setAge(A)] :- btdelete{P.age}, btinsert{P[age -> A]}.

Aspect Java Component for OBNFL:

Object-Oriented

Bayesian Network Full Learning

ORCAS: component assembly ORCAS: component assembly as rule base compositionas rule base composition

CHRD(OO(FL))

Rule Base

Object-Oriented

Bayesian Network

Full Learning:

Structure &

ParametersAspect J CHRD

Reasoning Explanation GUI

Aspect Java Contract Test

Generator Companion

Component for OBNFL

Generic Java

CHRD Base

Compiler

Generic Java

CHRD Base

Verifier

CHORD(RLEI)

Rule Bases

CHORD(BNI)

Rule Bases

CHORD(BNPL)

Rule Bases

CHORD(BILP)

Rule Bases

CHORD(BNFL)

Rule Bases

ORCAS: partnershipsORCAS: partnerships

CT-Info CNPq Partners

Profa. Patrícia Tedesco, CIn-UFPE Prof. Luis Menezes, UPE Prof. Jones Albuquerque, UFRPE

Focus Application case studies to intelligent agent tutoring system

(intersection with IATE), and marine ecology simulation (Simulare project)

ORCAS: partnershipsORCAS: partnerships

CT-Info FINEP Partners

Profa. Patrícia Machado, Prof. Jorge Figuereido, Prof. Franklin Ramalho, DSC-UFCG

Prof. Luis Menezes, UPE Jynx Playware (soccer and business simulation game company) CESAR

Focus Generation of built-in contract testing components from functional

component Automated generation of Transaction Frame Logic programs from

UML and OCL models (as a preliminary step towards generation of CHORD programs)

Application case studies to simulation games

ORCAS: partnershipsORCAS: partnerships

CAPES/DAAD PROBRAL Partners:

Prof. Silvio Meira, CIn-UFPE Prof. Luis Menezes, UPE Prof. Thom Frühwirth and PhD. student Marc Meister, Ulm Universität Prof. Colin Atkinson and PhD. student Matthias Guthiel, Mannheim

Universität Dr. Armin Wolf, Fraunhofer Gesellschaft Institute FIRST, Berlin

Focus Design of CHORD (syntax and formal semantics) Upgrade of KobrA process to latest OMG standards (UML2, OCL2, MOF2) Extension of KobrA process to specify GUIs Adaptation and extension of KobrA process for ORCAS components CASE tool for KobrA with model checking facilities First adaptive implementation of CHRD engine (compiled to Aspect J) Application case study on cost-saving surgery scheduling and allocation in

large hospital

ORCAS: current status and next ORCAS: current status and next stepssteps

Direct collaborators: PhD. candidate Jairson Vitorino, CIn-UFPE Undergraduate research assistant Marcos Aurelio Silva, CIn-UFPE Prof. Luis Menezes, UPE

Current status: KobrA model of preliminary, non-adaptive CHRD engine version Aspect J / Java implementation of the model Reasoning trace implemented as aspect First implementation as compiler to Java Novel, more efficient operational semantics

Next steps: Improve reasoning trace GUI Switch from chronological backtracking to conflict-directed

backjumping for disjunctive rule and finite domain labeling search

IATE: GoalsIATE: Goals

Build an extensible, fully parametric, multi-agent environment that can serve as reusable kernel for: Multi-Agent System (MAS) and Artificial Intelligence (AI) tutoring

environments; MAS and AI controlled experiments environments; MAS and AI based team ball sport computer games.

The environment: Simulates a single base domain, a futuristic fusion team ball sport

(rollerslam) In all its possible variations in terms of: observability, determinism,

dynamicity, physics modeling granularity, agent communication, as well as the size and diversity of agent action, agent perception, object and location sets.

Evaluate and extend MDA PIM transformation techniques to automatically generate product line of such environment

Motivation: AI and MAS teachingMotivation: AI and MAS teaching

Observable environment

Deterministic environment

Continuous environment

Real-time environment

Cooperative environment

Relational environment

MAS EnvironmentMAS Environment

Ontological commitment

Epistemological commitment

Variable domains

Knowledge RepresentationKnowledge Representation

Reflex agent

Past environment model agent

Goal agent Utility agent

Learning agent

Future environment model agent

Internal Agent ArchitectureInternal Agent Architecture

Deduction Abduction

Constraint solving

InductionAnalogy

Reasoning ParadigmReasoning Paradigm

Belief revision Inheritance

Motivation: AI and MAS educationMotivation: AI and MAS education

Didactic value of single domain illustrative example problems in multiple versions at progressive sophistication levels

Most existing problems: Fixed characteristics relevant to illustrate only a few techniques

Examples:

Partially observablePartially observable DiscreteDeterministicSequentialMono-agentOntologically poorOntologically poor

Partially observablePartially observable

ContinousNon-deterministicReal-TimeMulti-agentOntologically poorOntologically poor

IATE Motivation: AI and MAS IATE Motivation: AI and MAS experiments and competitionsexperiments and competitions

Fully parametric environment ideal benchmark generator to: Compare architectures, reasoning paradigms and knowledge

representation languages Pin-point which environment characteristics requires switching

technique What are the scalability phase transitions

Current competitions: Planning: restricted to very narrow technique and problem sets RoboCup: comparing “Cadillacs vs. Dinosaurs”, everything all at

once, no general scientific insight

IATE current status and next stepsIATE current status and next steps

Direct collaborators: PhD. candidate Fábio Moura, CIn-UFPE New undergraduate research assistant

Current status: Preliminary versions of:

MAS environment MOF metamodel and UML Profile Simplest rollerslam simulation UML model (instance of metamodel)

Next steps: More complex rollerslam simulation UML models Environment characteristics’ UML models Simulation independent rollerslam UML model Transformation rules to generate:

Simplest and more complex rollerslam simulation models from simulation independent rollerslam models and environment characteristics’ model

Repeat iteration for soccer simulations

MA

S E

nvir

on

men

t M

eta

-mod

el an

d U

ML

Pro

file

MA

S E

nvir

on

men

t M

eta

-mod

el an

d U

ML

Pro

file