peer-to-peer and
agent-based computing
Agent-Based Computing:
tools, languages and case studies
(Cont’d)
Agent Programming Languages
• Agent-oriented programming languages
– Not essential, BUT make life easier…
• Do you know of a specific functionality that can only be
implemented with a particular programming language?
– Special-purpose programming languages increase
productivity as they offer higher-level constructs
• Particular aspects of the targeted domain are easier to
explore
• Agent-oriented programming languages are useful
– However, there are many out there and many more are
appearing…
– Let’s look at some of them!
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AgentSpeak
• Influential abstract programming language
• Uses the BDI agent model (we’ll see more about it)
– Beliefs: what the agent believes about the environment
– Desires: what the agent needs to achieve
– Intentions: plans to fulfil the desires
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AgentSpeak Architecture
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AgentSpeak Control Loop
1. Agent receives events which are either
– External (from the environment or from sensors)
– Internally generated
Events (news and existing ones) are beliefs
2. Agent looks for plans that match events
– Matching plans become desires
3. Agent chooses one plan from its desires to execute
– Chosen plan is an intention
4. Execute chosen plan
– During execution of plan, new events arise – go to 1
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AgentSpeak: Beliefs
• Beliefs represent information the agent has
– About the environment
– About its own internal state
• Encoded as ground first-order logic predicates
sensor(temperature, 30)
sensor(smoke, low)
sensor(movement, detected)
status(light, on)
status(door, closed)
status(window, open)
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AgentSpeak: Plans
• Created off-line (manually or via planning systems)
• Provide agents with a description of the actions it
should perform:
– How to respond to events (e.g., if fire detected, then
enact the plan to put out fire and alert fire brigade)
– How to achieve goals (e.g., how to book a flight from
Dundee to Paris)
• Plan structure:
– Trigger condition: an event the plan can handle
– Context: conditions under which plan can be used
– Body: actual actions comprising the plan
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AgentSpeak: Plans (Cont’d)
• Format:
TriggeringEvent : Context <- Body
• Meaning:
– If TriggeringEvent arises and
– Context holds in the current set of beliefs then
– Execute Body
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AgentSpeak: Events
•
•
•
•
Achieve goal P: +!P
Drop goal P: -!P
Add new belief B: +B
Drop belief B: -B
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AgentSpeak: “Hello World”
/* Initial goals */
!start.
Agent has a single initial goal
/* Plans */
+!start : true <- .print("hello world.").
There is one single plan:
• if you have acquired goal “start” then
• when “true” holds (it always does)
• carry out the single step plan “print(“hello world”)
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Jason
• An interpreter of AgentSpeak in Java
• Development environment for MASs
• Free downloads:
– http://jason.sourceforge.net
• Integrated with JADE (distributed execution)
• Eclipse plug-in
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Java Extensions for Agent Programming
• Various extensions to Java
– Agent-specific constructs/built-ins
– JACK, JADE and JASON are Java extensions!
• There are, however, APIs which retain the syntax and
“object-orientedness” of Java
• Agent Building and Learning Environment (ABLE)
– IBM’s agent toolkit
– Java interfaces and base classes defining a library of JavaBeans
called AbleBeans, including
• Reading and writing text and database data,
• Rule-based inferencing using Boolean and fuzzy logic,
• Machine learning techniques (e.g., neural networks, Bayesian classifiers,
and decision trees).
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Case Study 1: Softbots
• Software robots (Softbots)
– Intelligent software agents that use software tools and
services on a person's behalf
– User communicates what she wants accomplished (the
goals), and softbots dynamically determine how to
satisfy the person’s request
• Tool use is one of the hallmarks of intelligence
– Softbots rely on the same tools and utilities available to
human computer users
– Tools for sending mail, printing files, searching the
Web…
• Popular Softbots interact with a wide range of
software tools and services through the WWW
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Case Study 1: MetaCrawler
• WWW search services operated by the Univ. of
Washington
– 1995–1997
– Licensed to Go2Net, now InfoSpace, in 1997.
• Single, central interface for WWW document
searching
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Case Study 1: MetaCrawler (Cont’d)
• Upon receiving a query:
1. It posts the query to multiple search engines in parallel, then
2. It performs sophisticated pruning on the responses returned
• MetaCrawler prunes as much as 75% of the returned
responses as irrelevant, outdated, or unavailable
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Case Study 1: MetaCrawler (Cont’d)
• Current version (www.metacrawler.com)
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Case Study 2: Calendar Agent
• Retsina Semantic Web Calendar Agent
– Developed at Carnegie-Melon’s Intell. Software Agents Lab
– Interoperability between RDF-based calendar descriptions on the
web, and Personal Information Manager (PIM) Systems such as
Microsoft's Outlook
– Home page: http://www.cs.cmu.edu/~softagents/cal/
• Schedules can be:
– Browsed and imported by the user manually
– Shared and imported autonomously by agents
• Assists users in keeping calendar “up-to-date”
– Higher fidelity model of user’s schedule
– Minimal cost on the user’s time
• Supports meeting scheduling
– Agents negotiate meeting times based on user’s schedule and
preferences
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Case Study 2: Calendar Agent (Cont’d)
• Schedules and events described on the web in RDF,
using ontologies, and linked to individual's contact
information (e.g., their home page).
• Consists of
– Distributed Meeting Scheduling Engine and
– RETSINA Semantic Web Calendar Parser.
• Agent
– Assists in organising and scheduling meetings between
several individuals,
– Coordinates these based on existing schedules
maintained by MS Outlook.
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Case Study 2: Calendar Agent (Cont’d)
• Agent interface and importing schedules onto Outlook
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Case Study 3: Robot Teams
• Teams of robots must coordinate
their actions and collaborate
– Example: robots playing football
– Each robot is an autonomous agent
• Robocup: yearly competition of
robot teams
– http://www.robocup.org
– Sony AIBO Legged League
• Football matches between teams of
autonomous quadruped robots made by
Sony Corporation.
– Sony AIBO's are publicly available and
can be programmed
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Further reading
• A Survey of Programming Languages and Platforms for
Multi-Agent Systems, Bordini et. al., 2006, available at
http://www.informatica.si/PDF/30-1/02_BordiniA%20Survey%20of%20Programming%20Languages%20and
%20Platforms...pdf
• Logics and Agent Programming Languages, Logan, B. and
Alechina, N., ESSLLI 2009, available at
http://www.agents.cs.nott.ac.uk/events/lapl09/
• JASON examples:
http://jason.sourceforge.net/wp/examples/
• IBM’s ABLE:
http://publib.boulder.ibm.com/infocenter/iseries/v5r3/ind
ex.jsp?topic=%2Frzahx%2Findex.html
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