kimas 2003 tutorial

KIMAS 2003 TutorialKIMAS 2003 Tutorial

Henry HexmoorHenry HexmoorUniversity of Arkansas

Engineering Hall, Room 328

Fayetteville, AR 72701

Crafting a Social Agent

September 30, 2003KIMAS 2003 Tutorial

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Content Outline

I. Introduction1. History and perspectives on MultiAgent Systems 2. Architectural theories3. Agent Oriented Software Engineering

II. Social agents4. Sociality and social models5. Dimensions for Developing a Social Agent Examples in Autonomy, Trust, Social Ties, Control, Team, Roles, Trust, and Norms

6. Agent as a member of a group...Values, Obligations, Dependence, Responsibility, Emotions

III. Closing

7. Trends and open questions8. Concluding Remarks



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Definitions

1. An agent is an entity whose state is viewed as consisting of mental components such as beliefs, capabilities, choices, and commitments. [Yoav Shoham, 1993]

2. An entity is a software agent if and only if it communicates correctly in an agent communication language. [Genesereth and Ketchpel, 1994]

3. Intelligent agents continuously perform three functions: perception of dynamic conditions in the environment; action to affect conditions in the environment; and reasoning to interpret perceptions, solve problems, draw inferences, and determine actions. [Hayes-Roth, 1995]

4. An agent is anything that can be viewed as (a)Perceiving its environment, and (b) Acting upon that environment [Russell and Norvig, 1995]

5. A computer system that is situated in some environment and is capable of autonomous action in its environment to meet its design objectives. [Wooldridge, 1999]



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Agents: A working definition

An agent is a computational system that interacts with one or more counterparts or real-world systems with the following key features to varying degrees:

• Autonomy

• Reactiveness

• Pro-activeness

• Social abilities

e.g., autonomous robots, human assistants, service agents



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The need for agents

1. Automation of dirty, dull, and dangerous as well as tedious, boring, and routine tasks to relieve humans of such duties. E.g., desktop assistants or intelligent in service of humans.

2. An improved human sense of “presence” for humans collaborating in physically disparate locations. E.g., knowledge management tasks like war-rooms and human users benefit from agents who proxy for their human counterparts.

3. Democratization of computing, services, and support. E.g., functions such as the department of motor vehicles or public libraries and virtual museums.

4. Reduction of redundancy and overlap due to competition. E.g., tracking and sharing power or telecommunication services.



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Agent Typology

• Person, Employee, Student, Nurse, or Patient• Artificial agents: owned and run by a legal entity • Institutional agents: a bank or a hospital• Software agents: Agents designed with software• Information agent: Data bases and the internet• Autonomous agents: Non-trivial independence • Interactive/Interface agents: Designed for

interaction• Adaptive agents: Non-trivial ability for change• Mobile agents: code and logic mobility



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Agent Typology

• Collaborative/Coordinative agents: Non-trivial ability for coordination, autonomy, and sociability

• Reactive agents: No internal state and shallow reasoning

• Hybrid agents: a combination of deliberative and reactive components

• Heterogenous agents: A system with various agent sub-components

• Intelligent/smart agents: Reasoning and intentional notions

• Wrapper agents: Facility for interaction with non-agents



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Falacies: What Agent-based Systems are not

• Computational X where X is from the social sciences such as the economics

• Agents are not middleware components• Agents are not Grid Services• Agents are not Internet software • Agents need not dwell online• Agent-based Systems are not necessarily decision-

support systems• Agent-based Systems do not necessarily employ AI

methods• Agents need not be implemented in specific

programming languages or paradigms



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Multi-agency

A multi-agent system is a system that is made up of multiple agents with the following key features among agents to varying degrees of commonality and adaptation:

• Social rationality

• Normative patterns

• System of Values

e.g., eCommerce, space missions, Intelligent Homes

The motivation is coherence and distribution of resources.



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Summary of Business Benefits

• Modeling existing organizations and dynamics

• Modeling and Engineering E-societies

• New tools for distributed knowledge-ware



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Two views of Multi-agency

Constructivist: Agents are rational in the sense of Newell’s principle of individual rationality. They only perform goals which bring them a positive net benefit without regard to other agents. These are self-interested agents.

Sociality: Agents are rational in the Jennings’ principle of social rationality. They perform actions whose joint benefit is greater than its joint loss. These are self-less, responsible agents.



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Multi-agent assumptions and goals

• Agents have their own intentions and the system has distributed intentionality

• Agents model other agents mental states in their own decision making

• Agent internals are of less central than agents interactions

• Agents deliberate over their interactions

• Emergence at the agent level and at the interaction level are desirable

• The goals is to find some principles-for or principled ways to explore interactions



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Abstract Architecture

action

Environment

actionsstates action



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Architectures

• Deduction/logic-based

• Reactive

• BDI

• Layered (hybrid)



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Abstract Architectures

An abstract model: <States, Action, S*A>

An abstract view

S = {s1, s2, …} – environment states

A = {a1, a2, …} – set of possible actions

This allows us to view an agent as a function

action : S* A



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Logic-Based Architectures

These agents have internal state See and next functions and model decision making by a set of deduction rules for inference

see : S Pnext : D x P Daction : D A

Use logical deduction to try to prove the next action to take Advantages

Simple, elegant, logical semantics Disadvatages

Computational complexityRepresenting the real world



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Reactive Architectures

Reactive Architectures do not use symbolic world model

symbolic reasoning

An example is Rod Brooks’s subsumption architecture Advantages

Simplicity, computationally tractable, robust, elegance Disadvantages

Modeling limitations, correctness, realism



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BDI: a Formal Method

• Belief: states, facts, knowledge, data

• Desire: wish, goal, motivation (these might conflict)

• Intention: a) select actions, b) performs actions, c) explain choices of action (no conflicts)• Commitment: persistence of intentions and trials

• Know-how: having the procedural knowledge for carrying out a task



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Beliefs

beliefrevision

generateoptions

Desiresfilter

Intentions

actsense

Environment

Belief-Desire-Intention



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A simplified BDI agent algorithm

1. B = B0;

2. I := I0;

3. while true do

4. get next percept ;

5. B := brf(B, ); // belief revision

6. D:=options(B,D,I); // determination of desires

7. I := filter(B, D, I); // determination of intentions

8. := plan(B, I); // plan generation

9. execute

10. end while



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Correspondences

• Belief-Goal compatibility:

Des Bel

• Goal-Intention Compatibility:

Int Des

• Volitional Commitment:

Int Do Do

• Awareness of Goals and Intentions:

Des BelDes

Int BelInt



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Layered Architectures

Layering is based on division of behaviors into automatic and controlled.

Layering might be Horizontal (I.e., I/O at each layer) or Vertical (I.e., I/O is dealt with by single layer)

Advantages are that these are popular and fairly intuitive modeling of behavior

Dis-advantages are that these are too complex and non-uniform representations



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Agent-Oriented Software Engineering

AOSE is an approach to developing software using agent-oriented abstractions that models high level interactions and relationships.

Agents are used to model run-time decisions about the nature and scope of interactions that are not known ahead of time.



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AOSE Considerations: Track 1

Programming platforms (e.g., JACK) to support not just programming and design

What, how many, structure of agent?

Model of the environment?

Communication? Protocols? Relationships? Coordination?



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AOSE Considerations: Track 2

Extending UML to support agent communication, negotiation etc.

Communication? Protocols? Relationships? Coordination?



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Gaia- Wooldridge, et al

The Analysis phase: Roles model:

- Permissions (resources) - Responsibilities (Safety properties and Liveliness properties)

- Protocols Interactions model: purpose, initiator, responder, inputs,

outputs, and processing of the conversationThe Design phase:

Agent modelServices modelAcquaintance model



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Scott DeLoach’s MaSE

Roles TasksSequenceDiagrams

Agent ClassDiagram

Internal AgentDiagram

ConversationDiagram

DeploymentDiagram



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Break– 5 minutes



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Content Outline


Break 5 minutes II. Social agents

4. Sociality and social models5. Dimensions for Developing a Social Agent Examples in Autonomy, Trust, Social Ties, Control, Team, Roles, Trust, and Norms

Break 5 minutes6. Agent as a member of a group...Values, Obligations, Dependence, Responsibility, Emotions

III. Closing




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A Multiagent System Top level loop

Initialize Groups, InterconnectionsFor agents 1- n { While (1) { Sense (self, world, others)

Reason (self, others)Act (physical, speech, social)

}}



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Inside an agent…

While (1) { Sense (self, world, others)

Determine attitude (self, others)Reason (self, others)Act (physical, speech, social)

}



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What is Sociality?

Think, Feel, Do Think, Feel, Do


• In interactions one individual’s thinking, feeling, and/or doing affects another individual.

• “” may involve a social action, a social convention, and a personal rationality.



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What is Sociality?



• An individual may engage collectives in interaction of thinking, feeling, and/or doing.

• “” may involve a social action, a social convention, and a unit rationality.



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What is Sociality?



• An agent may engage a human in interaction of thinking, feeling, and/or doing.

• “” may involve a social action, a social convention, and a personal rationality.



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What is Social Action?

• Social actions produce different kinds of influences.

• For example actions involving Resources, Delegation, Permission, Help, and Service.



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What is Social Convention?

• Social conventions prescribe transformations of social influences as well as shifts and changes in the transformations.

• Examples:• Interpersonal tactics such as reciprocity, scarcity, and

politeness.• Use of norms, values, plans, policies, protocols, and roles.• Following a conversational policy. • Emotional reactive responses• Cooperation logics• Adaptations and emergence rules



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What is Personal/Unit Rationality?

• Personal/unit Rationality prescribes stance of an individual or a collective toward social conventions with respect to others.

• An agent/collective might choose to follow or abandon social conventions either with all agents or selectively.

• Social Rationality versus Individual Rationality



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Putting it together (CEBACR): A social model of interaction

<Cognition,Emotions, Behaviors,

Social Actions, Social Conventions,

Personal/Unit Rationality, Embodiment>



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A Special Case of Do Do Sociality

• [Do] [Do]

• Actions are “buy” and “sell”• Social Conventions are conventions of bartering.• Personal/Unit Rationality is accounting for utilities of self or

others. This can be simple or extend to issues of reciprocity and goodwill.



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A Social Agent

• An agents that has to interact with people, other agent(s), where it is affected and can affect others’ cognitive states, emotions, and/or behavior via social actions, social conventions, a personal rationality.

• Generally, such agents are more complex than reactive agents and must include social perception in their deliberation.



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A Social Agent

• We cannot merely add social modules to prefabricated agents. Social makeup of such agents are found in all aspects of their architecture and must be designed from the start.

• We must at least have access to an agent’s social model:

<Cognition, Emotions, Behaviors, Social actions, Social Conventions, Personal Rationality>



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A Social Agent

Socially intelligent agents are biological or artificial agents that show elements of (humanstyle) social intelligence. The term artificial social intelligence refers then to an instantiation of human-style social intelligence in artificial agents. (Dautehahn 1998)



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Social Inference

Observing Interpersonal ExchangesGesture

Body Language

Emotions in communicationIllocution in communication

Goals and plans

Commonalities in goals and plans

BenevolenceSocial ties Psychological states

Inferred Attitudes and Relationships

Inferred Social Import Trust AutonomyPower CoherenceNorms ValuesTeam Control

Sub-cognitive

Cognitive

AttitudeCapability

Dependence



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Situatedness

• Physically situatedness promotes frequent sampling of physical environment, feedback via physical environment… as in the Subsumption architecture

• Socially situatedness promotes frequent sampling of environment (gossip), feedback via social interaction… to new agent architectures



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Levels of Sociality

• There are many MAS or HAI problems that are deterministic and would not require social reasoning. I.e., agent’s actions would not depend on others and if so it is pre-determined. At best, sociality is a luxury.

• There are scenarios where sociality, explicit reasoning about other agent’s or human actions are critical and it is not all predetermined. This requires high level of sociality.



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Social delegation

• E.g., X gives Y permission and authority to make decisions for their organization

• Social delegation differs from physical delegation in that agents will have a “cognitive” exchange in stead of a physical one.

• Models of social delegation might be economic (utilitarian), dependency (in-debtedness), power-based (authority), or democratic.



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Social Environment

Tasks;Resources; Ontologies

Public skills

Communication and exchange

Modeling other agents …

Social and collaborative notions

Emotions

Initiative, Autonomy, Power, Control, …

AnthropomorphismLanguage realism

Collaboration: Trust, safety, flexible roles, policies, preferences

Emotions

Awareness…

Emergent Norms and roles

Cultural shifts in institutions organizations

Adherence to norms, values, obligations, power, org rules…

Adaptation and changesin reasoning about basic social notions

HumanOrganization

Culture Multi-Agent

Dimensions for Developing a Social Agent

Communication and exchange

Planning and learning abilities

Team

Community



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Social Environment

HumanOrganization

CultureMulti-Agent

Dimensions for Developing a Social Agent

Team

Community

Asynchronous : Sit Aware : Real-timeCommunication

Info sharingCoordination



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Social Environment

Agents that are embedded in social environments must be designed to account for the following needs:

• Social tasks• Shared Resources • Ontologies• Public skills related to tasks and resources such as

requesting and delegating



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Agents in Public Service

Interactions with the public beyond individuals

Public libraries Museums Shopping malls Transportation stations Billboards and road signs



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Multiagent

Agents that can relate to other agents must be designed to account for the following needs:

• Communication and exchange of information,• Modeling other agents and rationality: altruism and benevolence,• Planning and learning abilities,• Social and collaborative notions: Autonomy, Values, Norms,

Obligations, Dependence, Control, Responsibility, Roles, Preference, Power, Trust, Teaming, Persona.

• Emotional communication.



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Agents in automation of dirty, dull, and dangerous tasks

Intelligent homes Factories Telecommunications Power Plants Investment Transportation Electronic Customer Relations Management Cross Organizational Relations

Multiagent: Shared Autonomy Among Personal Satellite Assistants

PSAs reason about commitments to teaming to respond to alarms



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Autonomy Sources

Capability

Social ties… benevolence, permissions, peer pressure (autonomy norm), reciprocity, norm sanctions



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Trust Can mean different things

Expectation of partner’s competence- Cristiano Castelfranchi

Expectation of partner’s benign intent- Diego Gambetti

Trust as a reputation and a recommendation- Mike Schillo

Correct Expectations about partner’s actions- Patha Dasgupta

Trust as reliable contract- Svet Brainov



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Social Ties

Social ties between agents affects social relationships. Trust and autonomy are increased with stronger ties. Communities are more robust with ties. Network structures embody collective properties of their

community.

Number of ties

Performance



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Models of Trust and Autonomy 2002

Trusting value(A, B, t) = Capability(B, t) + Benevolence(B, A, t) + Delegation harmony(A,B)Autonomy value (A, t) = Capability(A, t) + Average Trust (A) + Balance of reciprocity()

0

2

4

6

1 8 15 22

Time

Ave

rage

Tru

st &

A

vera

ge A

uton

omy

Tb

Ab

An

Tn



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Terraforming Mars 2002

Trust(Aj, Ak, t) = Trust(Aj, Ak, t-1) – (rate * Trust(Aj, Ak, t-1) + (rate * (gain - investment))



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Human

Agents that can relate to humans socially must be designed to account for the following needs:

• Communication and exchange of information,• Human intent and preferences, • Human need for anthropomorphic appeal,• Nested representations of humans and agents,• Human policies for interaction and guidance,• Collaborative requirements, and• Emotional communication



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Trust

Reasons for trust in agents:

Preference to delegate: an human operator might want another agent who has more time or resources to carry out a task

Human-agent relations: Agents can use human their understanding of human models of trust to interact with humans

• Human-Agent Interaction

• Adjustable Autonomy

Autonomy

HAI: Shared Autonomy between an Air Traffic Control assistant agent and the human operator

ATC agent and human operators learn to share and trade autonomies

HAI: UCAV formations

•UCAVs reason about helping in attack situations •HA power



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Organization

Agents that must operate in organizations must be designed to account for the following needs:

• Awareness of organizational rules, and structure, • Ability to evolve and recognize emergent norms and roles,

and• Adaptation and changes in reasoning about basic social

notions.



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Knowledge Management

Data storage and retrieval functions Indigenous ontologies Norms and Policies Institutions

Norms

• Involve two or more agents. Each agent understands and shares them.

• Agents have power to not choose them.

• There is no direct rational account of them available to the agents.

• The bearer experiences an implicit or an explicit sanction or rewards for adoption.

City grid - 2003

• Collisions cost agents time and intersections are out for a period.• Agents must reason about norms of stopping for traffic lights or not based on comparisons of their gains and losses relative to the society• Adaptive norm revision outperforms prescriptive norm assignment

Multiagent: Shared Autonomy Among Low-orbit Satellites

Satellites learn to recruit and form teams for collaborative image gathering

Roles

• Several agents can adopt it individually, independently, and concurrently. One agent may adopt several simultaneously. Several agents may adopt it as a group. In general we will call this the adopter.

• It is meaningful in the social context of other agents including (a) the adopter’s relationship to other agents and groups, (b) the agent’s mental attitudes about the social relationships, and (b) the available norms including obligations and responsibilities.

• There are typical capabilities associated with the adopter. If the adopter loses these abilities then the efficacy of the role is jeopardized.

Roles

• Networks of roles are more clearly seen in role-based access control.

• Role hierarchy and role grouping are useful for selecting subsequent roles [Moffett and Lupu, 1999, Na and Cheon, 2000].



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Culture

Agents that are culturally embedded must be designed to account for the following needs:

• Ability to reason about adherence to norms, values, obligations, organizational rules, etc., and

• Ability to recognize shifts in culture of their organizations and institutions



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Agent Historians and Dictionaries

Nuances of cultural shifts Norms Laws Institutions

Collaborative filtering



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Break– 5 minutes



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Content Outline





III. Closing




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Agent as a member of a group...

agent

group

values(terminal goals)

norms

partakes

relies on

member of

goalsspecifies

plans

obligations

honors

institution

organization

inheritspartakes set/borrow contains

shares

handles

partakes

roles

Values

• "value" might mean:(a)assessment of usefulness of an object or

action relative to a purpose, I.e., "(instrumental) evaluations", E.g., ="this knife is good for chip carving ",

(b) absolute assessment of desirability of something, I.e, “principles”, E.g., "honesty is good"

• Adding value to an agent enables it to generate internal desires as well as adds a level of behavior predictability for other agents.

Obligations

• Obligations capture all forms of social influence.

• Obligations have a strong deontological and motivational senses (more so than norms)

• Obligations are frequently assumed to have penalties associated with the failure to meet the obligation. We make no such assumption; some obligations may have sanctions and some may not.

Responsibilities

• There are several types of responsibility: (a)Responsibility to concerns an agent’s obligation

to perform an action. (b)Responsibility for concerns an agent’s

obligation to see that a state of affairs obtains. (c) Responsibility about is the agent’s obligation to

behave in accordance with its principles, which is general, abstract, and typically with respect to an agent’s immutable values.

Responsibilities, CAST project [Yen, et al. 2001]

•Agents are represented as nodes of a graph.

•One type of labeled directed edge is between two agents (A B), and it represents that A delegates to B or conversely B is responsible to A with respect to .

•The delegation relationships is non-reflexive, anti-symmetric, and transitive. The transitive property can be used to establish implied relationships.



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Obligationsab (i.e., responsibility)

Values Norms

Dependenceba

(Powerab , Controlab)

Autonomy

Delegationba

Trustba

Emerson, 1962Tuomela, 2000

Tuomela, 2000

Mayer, et al1995

The big picture



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Emotions

Emotions provide possibilities for bypassing chains of reasoning to protect the agent in dangerous situations or to enable it to work with agents that have not been beneficial in the past.

HAI: quick feedback by human or agent; human appeal

Multiagent: Appraisal of situations



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Emotions

Emotions Theories: correspondence between emotions and behavioral situations. Feeling good a or bad into emotions…

Personality Theories: Individual differences that affect emotional relationships



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Content Outline

I. Introduction1. History and perspectives on multiagents 2. Architectural theories3. Agent Oriented Software Engineering




III. Closing




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Content Outline

I. Introduction1. History and perspectives on multiagents 2. Architectural theories3. Agent Oriented Software Engineering

II. Social agents4. Sociality and social models5. Autonomy, Team, Values, Norms, Obligations, Dependence, Control, Responsibility, Roles, Trust, Emotions

III. Closing6. Trends and open questions7. Concluding Remarks



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Current Trends

Pervasive and emerging agent applications: agent mediated e-commerce, emotional agents, embodied agents, virtual characters, conversational agents, etc.

Standardization efforts: FIPA.

New Initiatives: semantic web initiative.

Agent tournaments: RoboCup, Trading Agent Competition.



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Concluding Remarks

There are many uses for Agents: These are highly intuitive and expressive Multiagent Systems: These provide methods for defining

institutions and working models of sociological theories

Many open problems area available Theoretical issues for modeling social elements such as

autonomy, power, trust, dependency, norms, preference, responsibilities, security, …

Adaptation and learning issues Communication and conversation issues



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Further Explorations

[email protected] Agents.umbc.edu http://www.AgentLink.org/ http://www.multiagent.com/ http://homepages.feis.herts.ac.uk/~comqkd/aaai-social.html

http://jasss.soc.surrey.ac.uk/5/4/4.html http://jom-emit.cfpm.org/ http://www.stephenmarsh.ca/ http://www.iiia.csic.es/ http://www.salford.ac.uk/cve/va99/on-line99.htm



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Further Explorations

http://orgwis.gmd.de/projects/SocialWeb/ http://bruce.edmonds.name/ssi/ http://www.casos.ece.cmu.edu/home_frame.html

http://bruce.edmonds.name/sfs/ http://jasss.soc.surrey.ac.uk/4/1/contents.html

http://www.isi.edu/teamcore/ http://www.ecs.soton.ac.uk/~nrj/soc-rat.html

kimas 2003 tutorial

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