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Understanding Agenticity and Multi-Agent Systems: Communication, Autonomy, and Learning, Slides of Artificial Intelligence

The concept of agenticity in programming, focusing on the characteristics of autonomous, communicative, and capable agents. It also discusses various types of agents and the need for a common framework through ontologies. Simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, and learning agents.

Typology: Slides

2012/2013

Uploaded on 04/29/2013

shantii
shantii 🇮🇳

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Introduction
1
As our programs become more “intelligent” we begin to
provide them with certain autonomy and certain
purpose.
These new programs in the parlor of AI are known as
“Agent” that is to say program with a purpose.
Yet what differentiates a program from an Agent? Well
that is a good semantic discussion in AI.
Let us for the time being just say that agent have a new
quality called “agenticy” and this new quality requires
certain characteristics in a program
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Download Understanding Agenticity and Multi-Agent Systems: Communication, Autonomy, and Learning and more Slides Artificial Intelligence in PDF only on Docsity!

Introduction

1

  • As our programs become more “intelligent” we begin to provide them with certain autonomy and certain purpose.
  • These new programs in the parlor of AI are known as “Agent” that is to say program with a purpose.
  • Yet what differentiates a program from an “Agent? Well that is a good semantic discussion in AI.
  • Let us for the time being just say that agent have a new quality called “agenticy” and this new quality requires certain characteristics in a program
  • Docsity.com

Agenticy in a program: Communication, autonomy, capability

2

  • Autonomous performance (e.g. threads)
  • Information handling (e.g. data base access)
  • Knowledge handling (e.g. beliefs, desires, intentions)
  • Goal seekers (i.e. purposeful)
  • Goal setters (i.e. purposive, very very very few)
  • Dynamic behaviors (i.e. not single I/O mapping)
  • May be embedded in another program
  • It is user enabled yet “empowered” to act on its own
  • Ability to work with other agents (i.e. multiple agents)
  • Ability to improve its behavior over time (i.e. learning)
  • Awareness and understanding of an environment (e.g. ontology driven)

MASs definition

Definition : A multi-agent system (MAS) consists of a network of loosely-coupled computational autonomous agents who can perform actions, they have resources at their disposal and they possess knowledge, capabilities or skills. They are situated in a common environment and they can interact through a set of rules, namely an interaction protocol.

It is rare for an agent to act in isolation and it is even more rare for an agent to be useful on its own

The need for a common framework:

Ontologies

5

Once we get involved with common knowledge then we have a problem of common understanding among programs ( aka agents ).

How do we standardized our concepts, this is in AI the field of ontologies.

What is the meaning of meaning?

Rather than using my own ppt I will use some links to explain this ever growing area of study

A semantic continuum

[Mike Uschold, Boeing Corp]

Shared human consensus

Text descriptions

Semantics hardwired; used at runtime

Semantics processed and used at runtime

Pump: “a device for moving a gas or liquid from one place or container to another”

(pump has (superclasses (…))

Implicit Informal (explicit)

Formal (for humans)

Formal (for machines)

Further to the right means:

  • Less ambiguity
  • More likely to have correct functionality
  • Better inter-operation
    • Less hardwiring
    • More robust to change
    • More difficult

Tightness of Coupling & Semantic Explicitness

Implicit, TIGHT

Explicit, Loose

Local^ 1 System: Small Set of Developers

Far

Same Process Space

Same Address Space

Same CPU

Same OS Same Programming Language

Same DBMS

Same Local Area Network Systems of Systems

Enterprise

Community

Internet

Same Wide Area Network Client-Server

Same Intranet

Federated DBs

Data Warehouses

Data Marts

Workflow Ontologies

Compiling

Linking

Agent Programming Web Services: SOAP

Distributed Systems OOP

Applets

Semantic Mappings (^) Semantic Brokers

Looseness of Coupling

Semantics Explicitness

XML, XML Schema

Conceptual Models

RDF/S, OWL Web Services: UDDI, WSDL

OWL-S

Modal Policies

Middleware Web

Peer-to-peer

N-Tier Architecture EAI

From Synchronous Interaction to Asynchronous Communication

Performance = k / Integration_Flexibility