2. Topics
• Introduction to DAI
• Multi-agent Systems
• Types of Multi-agent Systems
• Interaction among Agents
• KQML
• Basic Models of Communication
• Meta-Agent
3. ARITFICIAL INTELLIGENCE –
“the study and design of intelligent agents”
Artificial Intelligence (AI) is the area of computer science
focusing on creating machines that can engage on
behaviors that humans consider intelligent
"the science and engineering of making intelligent
machines.
- John McCarthy
4. AI contd.
The ability to create intelligent machines has intrigued
humans since ancient times, and today with the advent of
the computer and 50 years of research into AI
programming techniques, the dream of smart machines is
becoming a reality. Researchers are creating systems
which can mimic human thought, understand speech, beat
the best human chessplayer, and countless other feats
never before possible. Find out how the military is applying
AI logic to its hi-tech systems, and how in the near future
Artificial Intelligence may impact our lives.
6. DAI
Distributed artificial intelligence (DAI) is a subfield of
Artificial intelligence research dedicated to the development
of distributed solutions for complex problems regarded as
requiring intelligence. DAI is closely related to and a
predecessor of the field of Multi-Agent Systems
7. Torsun:
“Distributed Artificial Intelligence is a branch of Artificial
Intelligence that is concerned with cooperative problem solving by a
decentralized group of collaborating agents. Agents are logically
independent and autonomous, they reason, plan, learn and communicate”
distributed system of a collective architecture (domain of distributed
problem solving), decomposes the problem among collection of
autonomous, cooperating, knowledge sharing agents, case specific
distributed system of an integration architecture (a multi-agent systems)
integrates number of heterogeneous agents of a different nature,
complexity reduction
8. Agent
Jennings: "An agent is a computational system, situated in
some environment, that is capable of intelligent,
autonomous action in order to meet its design objectives."
cooperate learn
autonomous
interface agent
collaborative agent
collaborative
learning agent
intelligent agent
9. Agent’s Intelligence
Reactivity – Agents ability to provide intelligent responses to percepts
and agent senses from the environment (user interface)
Proactivity – Agents ability to maintain long term intention, organize its
behavior in order to meet targeted goals
Social Intelligence –Their ability to perform reasoning about other
agents abilities, intentions, current status and possible future course of
actions
Reactive Agent – agent presenting a reactive intelligence only
Cognitive Agent – manipulate symbolic model about their own status,
and capabilities (proactivity) and about its collaborative environment
10. Agent’s Abstract Architecture
• Agent’s Communication Wrapper
– translation to and from ACL (Agent
Communication Language)
– physical connection and responsibility
delegation
– perception × action
– social model
A G E N T ' S
B O D Y
A G E N T ' S
W R A P P E R
11. Agent is not just a Program
• an agent in the context of Distributed Artificial
Intelligence is a member of a multi-agent
community, where its behaviour and logic behind
reasoning has to bee seen from the multi-agent
perspective
• freely interact, interaction among agents is
emergent
• can group into coalitions, teams, they can benefit
from this
• do not have to be benevolent, have free will, can
cheat
12. • can leave/join the community
• can adapt and improve their social role
• however there are also other agents such as migrating
agent, viruses, information seekers … who are not
members of multi-agent community in the above sense
13. Agent is not an Object
• Objects and Agents both
– hide information, allow distributed tractability
– provide interaction mechanisms among system components
• Objects unlike Agents
– are passive, do not operate unless activated
– are too grainy structured (ABS: System – Community – Agent)
– exchange too primitive imperative message, (agents
communicate declarative knowledge)
– do not support flexible organisational relationship, the systems
object model is fixed
– present designed behaviour, while agent are expected to form
they own patterns behaviour emergently (from interaction)
14. Classification of Agents
• Reactive agent - A reactive agent is not much more than
an automaton that receives input, processes it and
produces an output.
• Deliberative agent - A deliberative agent in contrast
should have an internal view of its environment and is
able to follow its own plans.
• Hybrid agent - A hybrid agent is a mixture of reactive and
deliberative, that follows its own plans, but also
sometimes directly reacts to external events without
deliberation.
15. Reactive Agents
• Reactive agents are agents that do not contain any symbolic knowledge
representation (ie: no state, no representation of the environment, no
representation of the other agents, ...). Their behaviour is simply defined by
a set of perception-action rules.
• Subsumption architecture – System of ordered layers of perception-action
rules, where the lower layers take precedence.
Example of rock sample collecting robots (Steels), inspired by ants:
if detect an obstacle then change direction
if carrying samples and at the base then drop samples
if carrying samples and not at the base then travel up
if detect a sample then pick sample up
if true then move randomly
16. Cognitive Agents
• Cognitive Agents are agents with an explicit knowledge
representation of own capability, other agents, the environment, etc.
• There are various models of agents’ cognitive states (differ in
purpose, generality)
– BDI (Belief Desire Intention)
– Joint Intentions Theory
– 3bA (Tri-Base Acquaintance Model)
17. Interaction among Agents
• Organization
– an arrangement of relationships between individuals
or components, division of tasks, distribution of
roles, and contribution-awards
• Cooperation
– sharing responsibilities in satisfying shared goal and
generating mutually dependent roles in joint
activities
• Coordination
– management of agents activities so that they
coordinate their deeds with each other in order to
share resources, meet their own interests
18. Interaction among Agents cont’
• Negotiation
– information exchange aimed at resolving
conflict of access to resources, different
solutions to the same problem or goal
conflicts
• Communication
– information, knowledge and request exchange
via mutually agreed ACL
• Benevolence
– agent are benevolent if they will agree to
cooperate in asked/required
19.
20. Agent Communication Language
In order to ensure agent interoperability, mutually agreed
communication protocol (ACL) must be provided
• Mutual knowledge understanding
– translating from one knowledge representation
language into another
– sharing of semantics (and often pragmatics)
• Inter Agent Communication
– transport protocol (e.g. TCP/IP, SMTP, HTTP, …)
– communication language
– interaction protocol
21. Knowledge Sharing Effort
is an initiative addressing agent interoperability and
knowledge sharing
• Ontolingua - a software tool and methodology for -
means for sharing semantics (and often pragmatics) of
represented knowledge;
• Knowledge Interchange Format (KIF) - an inter-
lingua for translations between different knowledge
representations;
22. • Knowledge Query and Manipulation Language
(KQML) - a language for communicating attitudes
about the shared knowledge.
• Ontologies-KIF-KQML is sometimes denoted as ACL.
An ACL message is a KQML expression where
arguments are KIF sentences formed from terms from
appropriate ontologies.
• Foundation for Intelligent Physical Agents
Organization (FIPA).
24. Basic Models of Communication
• Broadcasting of a task announcement
– autonomous communication
– communication intensive
• Central Communication Agent
– well organized, saves communication
– central, fragile, communication bottleneck
• Acquaintance Models
– model of the environment in an abstract sense
