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Impressions of EASSS, Workshops & AAMAS. Jan Dijkstra. EASSS European Agents Systems Summer School. An initiative of AgentLink AgentLink  Europe’s IST-funded Network of Excellence for Agent-Based Computing 150 participants. Topics. EASSS. AgentLink – Michael Luck

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slide1
Impressions of

EASSS, Workshops

& AAMAS

Jan Dijkstra

easss european agents systems summer school
EASSSEuropean Agents Systems Summer School
  • An initiative of AgentLink
  • AgentLink 

Europe’s IST-funded Network of Excellence for Agent-Based Computing

  • 150 participants
topics
Topics

EASSS

  • AgentLink – Michael Luck
  • Introduction to Agents / Intelligent Agents – Michael Wooldridge
  • Logical Foundations of Agent-based Systems –

o.a. John-Jules Meyer

  • Agent-bases Simulation of Societies – Nigel Gilbert
  • Agent Mediated Electronic Commerce – Frank Dignum
  • Intelligent Information Agents for the Internet – Mathias Klusch
  • Agent Oriented Software Engineering Methodologies –

o.a. Onn Shehory

  • Agent Autonomy – Henry Hexmoor
  • Business Applications of Autonomous Agents and Multi-Agent Systems – Jorg Müller
  • Economically Founded Multiagent Systems – Tuomas Sandholm
  • Coordination in MAS – Ed Durfee
aamas
AAMAS
  • A week-long program that addressed all the key issues in contemporary agent systems research
    • 16 Workshops
    • 13 Tutorials
    • 44 Sessions
    • Ca 170 paper presentations
    • Ca 110 poster presentations
    • 700 delegates
topics1
Topics

AAMAS

  • Agent Oriented Software Engineering
  • Bidding and Bargaining Agents
  • Trust and Reputation
  • Self-Organizing Systems
  • Markets And Auctions
  • Multiagent Simulation
  • (Mobile) Embodied Agents
  • Group and Organizational Dynamics
  • Robot Architectures
  • Argumentation, Persuasion, and Deception
  • Agent Communication Languages and Protocols
  • Formalisms and Logics
  • Applications in Commerce
  • Management of Computation
agentlink
AgentLink

EASSS

  • AgentLink coordinates research and development of agent-based computer systems on the behalf of the European Commission
    • Industrial action
    • Research coordination
    • Teaching and training
    • Special interest groups
    • Information structure
  • http://www.AgentLink.org
  • http://agents.umbc.edu
what is an agent
What is an agent?

EASSS

AgentLink

  • A good question, and one that receives an inordinate amount of time from within the agent community itself. Rather as was the case in the early days of object-oriented development, there is no one universally accepted definition of an agent, but instead a lot of people working in closely related areas, using similar ideas.
what is an agent1
What is an agent?

EASSS

AgentLink

  • Put crudely, an agent is an autonomous software system: a system that can decide for itself what it needs to do.
  • Two main “religions” appear:
    • Intelligent agents community

Intelligent agents are agents that can do reasoning or planning.

    • Mobile agents community

Mobile agents are agents that can transmit themselves across a computer network and recommence execution on a remote site.

overview
Overview

EASSS

AAMAS

  • Five ongoing trends have marked the history of computing
    • Ubiquity
    • Interconnection
    • Intelligence
    • Delegation
    • Human-orientation
  • Programming has progressed through
    • Sub-routines
    • Procedures & functions
    • Abstract data types
    • Objects

to agents.

Wooldridge

slide10
EASSS

AAMAS

  • An agent is a computer system that is capable of independent action on behalf of its user or owner.
  • A multiagent system is one that consists of a number of agents, which interact with one-another
  • In order to successfully interact, agents need ability to cooperate, coordinate, and negotiate.

Two key problems

    • How do we build agents that are capable of independent, autonomous action in order to successfully carry out the tasks that we delegate to them?
    • How do we build agents that are capable of interacting (cooperating, coordinating, negotiating) with other agents in order to successfully carry out the tasks that we delegate to them, particularly when the other agents cannot assumed to share the same interests/goals?

Wooldridge

what is an agent2
What is an agent?

EASSS

AAMAS

  • The main point about agents is they are autonomous: capable of acting independently, exhibiting control over their internal state.
  • Thus: an agent is a computer system capable of autonomous action in some environment.
  • An intelligent agent is a computer system capable of flexible autonomous action in some environment.

By flexible, we mean:

    • Reactive
    • Pro-active
    • Social

Wooldridge

other properties
Other properties

EASSS

AAMAS

  • Other properties, sometimes discussed in the context of agency:
    • Mobility
    • Veracity
    • Benevolence
    • Rationality
    • Learning / adaptation

Wooldridge

agents and objects
Agents and Objects

EASSS

AAMAS

  • Are agents just objects by another name?
  • Object:
    • Encapsulates some state
    • Communicates via message passing
    • Has methods, corresponding to operations that may be performed on this state
  • Main differences
    • Agents are autonomous
    • Agents are smart
    • Agents are active

Wooldridge

environments
Environments

EASSS

AAMAS

  • Accessible vs. inaccessible
  • Deterministic vs. non-deterministic
  • Episodic vs. non-episodic
  • Discrete vs. continuous
  • Static vs. dynamic

Wooldridge

agents as intentional systems
Agents as Intentional Systems

EASSS

AAMAS

  • Human behavior is often predicted and explained through the attribution of attitudes, such as believing and wanting, hoping, fearing, and so on.
  • The attitudes employed in such folk psychological descriptions are called the intentional notions.
  • The term intentional system describes the entities ‘whose behavior can be predicted by the method of attributing belief, desires and rational acumen’.
intentional stance 1
IntentionalStance 1

EASSS

AAMAS

  • Daniel Dennett coined the term intentional system to describe entities ‘whose behavior can be predicted by the method of attributing belief, desires and rational acumen’.
  • He identifies different ‘grades’ of intentional system:
    • A first-order intentional system has beliefs and desires but no beliefs and desires about beliefs and desires.
    • A second-order intentional system is more sophisticated; it has beliefs and desires about beliefs and desires-both those of others and its own.
intentional stance 2
IntentionalStance 2

EASSS

AAMAS

  • Intentional notions are abstraction tools, which provide us with a convenient and familiar way of describing, explaining, and predicting the behavior of complex systems.
  • Most important developments in computing are based on new abstractions:
    • Procedural abstraction
    • Abstract data types
    • Objects

Agents, and agents as intentional systems, represent a further, and increasingly powerful abstraction.

  • So agent theorists start from the view of agents as intentional systems: one whose simplest consistent description requires the intentional stance.
intentional stance 3
IntentionalStance 3

EASSS

AAMAS

  • This intentional stance is an abstraction tool – a convenient way of talking about complex systems, which allows us to predict and explain their behavior without having to understand how the mechanism actually works.
  • Now, much of computer science is concerned with looking for abstraction mechanisms
    • Therefore why not use the intentional stance as an abstraction tool in computing – to explain, understand computer systems?
post declarative systems
Post-Declarative Systems

EASSS

AAMAS

  • In procedural programming, we say exactly what a system should do;
  • In declarative programming, we state something that we want to achieve, give the system general information about the relationships between objects, and let a built-in control mechanism figure out what to do;
  • With agents, we give a very abstract specification of the system, and let the control mechanism figure out what to do, knowing that it will act in accordance with some built-in theory of agency.
abstract architectures for agents
Abstract Architectures for Agents

EASSS

AAMAS

  • Assume the environment may be in any of finite set Eof discrete, instantaneous states:

E={e,e’,…}

  • Agents are assumed to have a repertoire of possible actions available to them, which transform the state of the environment:

Ac = {α,α’,…}

  • A run, r, of an agent in an environment is a sequence of interleaved environment states and actions:
state transformer functions
State Transformer Functions

EASSS

AAMAS

  • A state transformer function represents behavior of the environment:
  • If (r) = , then there are no possible successor states to r. in this case, we say that the system has ended its run.
  • Formally, we say an environment Env= E,e0, where: E is a set of environment states, e0 E is the initial state; and is a state transformer function.
agents systems
Agents, Systems

EASSS

AAMAS

  • Agent is a function which maps runs to actions:

Ag :RE  Ac

An agent makes a decision about what action to perform based on history of the system that it has witnessed to date. Let AGbe the set of all agents.

  • A system is pair containing an agent and an environment. any system will have associated with a set of possible runs; the set of runs of agent Ag in environment Envby R (Ag , Env ).
perception
Perception

EASSS

AAMAS

  • The seefunction is the agent’s ability to observe its environment whereas the action function represents the agent’s decision making process.
  • Outputof the seefunction is a percept:

see : E  Per

which maps environment states to percepts, andactionis now a function

action: Per* A

which maps sequences of percepts to actions.

  • Next function maps an internal sate and percept to an internal state ; tasks etc.
utilities functions
Utilities Functions

EASSS

AAMAS

  • One possibility: associate utilities with individual states – the task of the agent is then to bring about states that maximize utility.
  • The optimal agent Ag in an environment Envis the one thatmaximizes expected utility.
logics for multiagent systems
Logics for Multiagent Systems

EASSS

AAMAS

  • Theorists conceptualize agents.
  • Different attitudes may be used to characterize agents.
  • Attitudes can be formalized; modal logic can be used as a tool for reasoning about attitudes, focusing on knowledge/belief.
  • That gives a semantics to the architectures, languages, and tools – literally, a meaning.
attitudes
Attitudes

EASSS

AAMAS

  • Information attitudes
      • Belief
      • Knowledge
  • Pro-attitudes
      • Desire
      • Intention
      • Obligation
      • Commitment
      • Choice
      • ……
  • Most-studied aspect of practical reasoning agents:

interaction between knowledge and action

agent oriented software engineering
Agent-Oriented Software Engineering

EASSS

AAMAS

EASSS by Onn Shehory and Franco Zambonelli

WS organized by James Odell and Gerhard Weiss

  • The concept of an agent as an autonomous system, capable of interacting with other agents in order to satisfy its design objectives, is a natural one for software designers. This led to a growth of interest in agents as new paradigm for software engineering.
  • Topics
    • Methodologies and Tools
    • Analysis, Design and Requirements Engineering
    • UML and Agents Systems
    • Patterns, Architecture en Reuse
key characteristics of agents se viewpoint
Key Characteristics of Agents (SE Viewpoint)

EASSS

AAMAS

  • Basic
    • Autonomy & Proactivity
    • Situatedness
    • Interactivity
  • Additional
    • Mobility & Locality
    • Openness
    • Learning & Adaptive Capabilities

AOSE

agent autonomy
Agent Autonomy

EASSS

AAMAS

  • Process-based and Object-based applications
    • Global goal achieved via global control scheme for the application entities
  • Agent-based applications
    • Sub-goals assigned to autonomous agents integrating execution capabilities
    • Implies perceiving agents as proactive entities
    • Multiple independent loci of control in applications
  • SE advantages
    • Control encapsulation as a dimension of modularity

AOSE

agent situatedness
Agent Situatedness

EASSS

AAMAS

  • Agents typically perceive a portion of the external world- an ‘environment’
    • Physical environment
    • Computational environment
  • They have to sense and effect
    • By perceiving what’s happening in the environment and possibly influencing it
  • SE advantages
    • Clear separation of concerns between
      • The active computation parts of the system (agents)
      • The resource of the environment

AOSE

agent interactivity
Agent Interactivity

EASSS

AAMAS

  • Agents maye execute in multiagent contexts and interact which each other
    • Agent communication
    • Agent coordination
  • Collaborative or competitive interactions
    • Agents interact to achieve a common goal
    • Competition as a peculiar form of collaboration
  • SE implications
    • Not a single characterizing protocol of interaction

AOSE

agent mobility locality
Agent Mobility & Locality

EASSS

AAMAS

  • Autonomous components can migrate across different multi-agent systems (or contexts)
  • Non-Functional Motivations
    • An be sometimes used to reduce bandwidth (local access to data and services)
    • Robustness
  • SE Motivations
    • Additional dimension of autonomous behavior
    • Improve locality in interactions

AOSE

openess of multiagent systems
Openess of Multiagent Systems

EASSS

AAMAS

  • The agents in a system may not be fixed
    • New agents can be created or enter a multiagent systems context
    • Mobile agents can arrive
  • Technological implications
    • Need of standards (e.g. FIPA)
  • SE implications
    • Controlling self-interested agents

AOSE

learning and adaptive agents
Learning and Adaptive Agents

EASSS

AAMAS

  • When agents have to be “intelligent”
    • They must be possibly able to learn from previous experiences
    • Improving the effectiveness of their actions
  • When agents lives in dynamic scenarios
    • They must be able to adept their behavior to changing situations
    • Re-shaping themselves
  • SE is not concerned with
    • HOW learning and adaptiveness are achieved
  • But it may be concerned with
    • WHAT could be the impact on the global software system of having components that change their behavior dynamically?

AOSE

agent oriented software engineering methodologies
Agent Oriented Software Engineering Methodologies

EASSS

AAMAS

  • KAOS, Tropos, Prometheus, GAIA, AUML, DESIRE, MaSE, ROADMAP, OPM/MAS etc.
  • Why a new method
    • Development of agent systems requires a new way of thinking about design
    • Goals, agents, plans rather than objects & methods

AOSE

tropos
Tropos

EASSS

AAMAS

Two key features

  • The use of knowledge level concepts , such as agent, goal, plan and other through all phases of software development
  • A pivotal role assigned to requirements analysis when the environment and the system-to-be is analyzed

AOSE

tropos phases
Tropos phases

EASSS

AAMAS

  • Requirements
    • Early requirements : identifying roles along with their goals
    • Late requirements : the system-to-be is introduced as another actor and is related to stakeholder actors in terms of actor dependencies
  • Architecturaldesign
    • More system actors are introduced and they are assigned subgoals or subtasks of the goals and tasks assigned to the system
  • Detailed design
    • System actors are defined in further detail, including specifications of communication and coordination protocols
  • Implementation
    • Specification is transformed into a skeleton for implementation

AOSE

tropos concepts and models
Tropos concepts and models

EASSS

AAMAS

  • Actor : an actor represents a physical agent or a software agent as well as a role or a position
  • Goal : strategic interests of actors
  • Dependency : one actor depends on another in order to attain some goal, execute some plan, or deliver a resource
  • Plan : represents a way satisfying a goal
  • Resource : represents a physical or an informational entity that one actor wants and another can deliver
  • Capability : represents the ability of an actor to define, choose and execute a plan fulfill a goal, given a particular operating environment
  • Belief : are used to represent each actor’s knowledge of the world

The abstract syntax of the language is defined in

terms of UML.

AOSE

implementing issues
Implementing Issues

EASSS

AAMAS

  • Object-oriented tools: are very much related to the object-oriented approach
    • JADE (Java Agent DEvelopment framework)
  • BDI toolkits: are based on BDI models
    • ParADE (Parma Agent Development Environment)

Toolkit for the development of FIPA agents

      • Agent level
        • Agents are atomic components
        • UML is used to build models of single agents and of the multiagent system
      • Object level, exploits the generated code:
        • Each agent is an object-oriented system
        • ParADE provides is a framework on top of JADE

AOSE

social interaction with embodied agents
Social Interaction With Embodied Agents

EASSS

AAMAS

  • Lewis Johnson

Director, CARTE USC / ISI

  • The CARTE research group at USC has conducted extensive research in animated agents
  • Strengths and weakness of agents are attributable to SI (or lack thereof)
  • This has lead to a research focus on
    • Social interaction design
    • Social agent modeling
embodied interfaces
Embodied Interfaces

EASSS

AAMAS

  • Embodied, human-like interfaces are increasingly common
    • Animated characters
    • Humanoid robots
  • It is useful to model these as agents
    • To support dynamic interaction
  • Possible roles:
    • Guides, tutors, teammates, story characters
  • Applications:
    • Education
    • Commerce
    • Entertainment

Lewis Johnson

claims
Claims

EASSS

AAMAS

  • Embodied interfaces raise the expectations of
    • Ability to understand user’s problem solving
    • Social interaction skills, i.e., social intelligence
  • Animated agents can meet these expectations
  • Benefits:
    • Improved user performance
    • Improved subjective experience
      • In a mutually reinforcing way

Lewis Johnson

characteristics of social agents
Characteristics of Social Agents

EASSS

AAMAS

  • Cognizance of other agents
  • Sensitivity to social relationship, roles
  • Sensitivity to social context, exchange
  • Sensitivity to social norms
  • Holding beliefs, attitudes about others
  • Aware of beliefs, attitudes of others
  • Able to manage interactions, taking above into account

Lewis Johnson

social intelligence implies
Social Intelligence Implies:

EASSS

AAMAS

  • Ability to model other agents
  • Ability to engage in natural interaction
    • Mixed-initiative interaction
    • Face-to-face interaction
  • “Emotional intelligence”
    • Ability express emotion appropriately
    • Ability to react to emotions in others
  • Ability to develop social relationships
    • Convergence and adaptation of behavior
    • Shared experience

Lewis Johnson

application of animated social agents
Application of Animated Social Agents

EASSS

AAMAS

  • Virtual reality training environments
    • Application: military training
  • On-line education
    • Applications: medicine, dentistry, chemistry, engineering, elementary school science
  • Interactive pedagogical dramas
    • Applications: health education for adults and children, military training

Lewis Johnson

steve
Steve

EASSS

AAMAS

Lewis Johnson

new version of steve
New version of Steve

EASSS

AAMAS

Lewis Johnson

steve s social intelligence
STEVE’s Social Intelligence

EASSS

AAMAS

  • Capabilities:
    • Use of verbal and nonverbal cues to indicate awareness
    • Face to face multimodal communication
    • Mixed initiative dialog
    • Appropriate emotional reactions
  • Capabilities as yet insufficient:
    • Ability to model the learner’s state, characteristics based on interactions
    • Ability to learn from interactions with the student

Lewis Johnson

steve architecture
Steve Architecture

EASSS

AAMAS

Cognition

Lewis Johnson

Perception snapshot,

important events

Abstract motor commands

Motor

Control

Perception

Spatial information

Relevant events

Detailed motor commands

Simulated

World

Steve

Body

adele
Application: case-based health science courseware

Monitors students, gives advice, hints, feedback

Intervenes if the student makes serious mistakes

Records and evaluates student performance

Adele

EASSS

AAMAS

Lewis Johnson

agent based analysis and simulation of dynamics in biological cognitive and organizational domains
Agent-Based Analysis and Simulation of Dynamics in Biological, Cognitive and Organizational Domains

AAMAS

Jonker

Treur

Agent-based modeling makes the inherent complexity of the dynamics of multiple, interacting active processes manageable by choosing an appropriate level of abstraction in describing them. It offers structuring of a dynamic phenomenon into: internal processes within an agent, externally observable behavior of an agent, and organizations of multiple agents.

Techniques are available for specification of dynamic properties, simulation, and formal analysis of dynamics. Relations between dynamic properties at different levels can be identified, such as

  • Are certain dynamic properties observed and specified at a certain level of agent behavior
  • Which dynamic properties of agent behavior fit in a certain role within a given organizational model
  • Does an observed trace of dynamics of an agent organization fulfill a specified dynamic property
  • If an organization does not show appropriate dynamics, which part is to blame?
literature about agents
Literature about Agents
  • Stuart Russell & Peter Norvig

Artificial Intelligence - A Modern Approach

  • Gerhard Weiss (ed.)

Multiagent Systems – A Modern Approach to Distributed Artificial Intelligence

  • Jacques Ferber

Multi-Agent Systems

  • Michael Huhn & Munindar Singh (eds.)

Readings in Agents

  • Michael Wooldridge

Reasoning about Rational Agents

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