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Artificial Intelligence and Multi-Agent
Systems Ana García-Serrano PROMAS, AL3
TF2 Ljubljana, 28 Feb. 2005
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Software Engineering and Knowledge Engineering
(AI)
USE ONLY WHEN NEEDED!
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Intelligent agent (from AOSE AL3 TF2)
As the systems becomes complex it is needed
abstractions and metaphors to explain their
operations. INTELLIGENT AGENT A cognitive agent
that is proactive (through an analytical or
reactive operations ie decision) and use a
representation of the environment AND - has a
representation of N (possible 0) other agents
(users or agents) - is endowed with an extensive
domain model AND ALSO Learning (acquire the
knowledge it needs to his operation/reasoning) Dee
p Understanding of emotions BUT the designer
has to recognise the opportunity for employing an
intelligent agent and trust on its competences
We dont want to solve all the problems in AI
BUILD USEFUL AGENTS!
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Anatomy of a cognitive-intelligent agent
COMMUNICATION LAYER
KNOWLEDGE
METHODS
Reasoning about problems. Perception of the
virtual or physical environment Interaction
Protocols - subtasks assignment - resources
competition - sharing of tasks

• Capacity for problem identification
• Internal capacity of problem solving reactive
  or analytical
• Knowledge about the
• (perceived/ known) structure
• of other agents
• Strategic knowledge for rational decisions
  (agenda, utilities)

Individual model
Social model
AGENDA prioritized sequence of tasks, Ti, Tj,
Tk, Tp, ...
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Agent-based Engineering

• Agent concept in fashion during last decade as
  any software system
• rational and autonomous action in a (changing)
  environment
• able to interact into a network (of possible 0
  nodes)
• Agent based systems (problem centred approach)
• A very useful paradigm to cope with dynamic
  interactions between distributed resources,
  distributed task execution, legacy systems
• Sets of benevolent agents with shared goals
• The modularity allows changes and facilitates the
  upgrade and recovering from unexpected situations
  
• USE WHEN REALLY NEEDED! (lower cost of
  centralized solution)

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Intelligent assistance to e-commerce The ADVICE
project
IS THERE A PROBLEM TO SOLVE?

• The e-commerce solutions has to be improved given
  that
• Mainly focus on the presentation of goods
• The interaction is guided by the user
• From the customer point of view the search and
  the selection of products is a difficult task due
  to the lack of assistance

IS ADECUATE THE USE OF Intelligent AGENTS?

• An agent-based architecture reflects the
  conceptual and functional distribution of the
  decision support installed as a top layer of
  legacy system
• Intelligent agent to model the sales business
• Interaction agent to user/system mixed initiative
• Interface agent to multimedia input/output that
  satisfies the user

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Knowledge engineering

ATN Decision tree
Multimedia planner
Ontology KB Rules
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Working prototype in Ciao Prolog and Java
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Working prototype in Ciao Prolog and Java
(multi-user)
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MAS Engineering

• Agent concept in fashion during last decade as
  any software system
• rational and autonomous action in a (changing)
  environment
• able to interact into a network (of possible 0
  nodes)
• Multi-agent systems (interaction centred
  approach)
• A very useful paradigm to the deployment of
  inherently complex (no-structured) applications
  in inherently distributed environments
• Heterogeneous agents in any kind of organization
  or society
• Harmonization of the interaction between active
  agents

USE WHEN REALLY NEEDED! (the centralized solution
is better
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MAS Traffic Control Agents TRYSA2

• TRYSA2 convert the original benevolent TRYS
  agents into rational agents
• TRYS embedded agents produces
• executable local signal plans with
• local utility value
• Structural co-operation
• - Normative layer permissions and prohibitions
• to use control devices
• - Social layer distributed search for the
  global signal plan that corresponds to the
  bargain outcome (efficient and fair)
• Robust and scalable solution reaching a lower
  quality solution than the agent based with
  coordinator

a
gent
a
gent
a
gent
agent
- 1.000 lines of C - 5.000 lines of prolog -
500 lines of Tcl/Tk
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Traffic Control Agents TRYS
Generic Structure of the TRYS decision model
Coordination Agent integrates local control
proposals into global consistent signal plans
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