do software agents know what they talk about?

Do software agents know what they talk about?

Agents and Ontology

dr. Patrick De Causmaecker, Nottingham, March 7-11,

2005

Nottingham, March 2005

Agents and Ontology [email protected] 2

Overview Agents Ontology Communication RDF Semantic Web Sample Implementations



Agents Examples Definitions Properties Why Agents Pitfalls Models Architectures Standards



Examples Tim Berners-Lee example of

negotiating Agents Agents in a Route Planning

Application Tele Truck Planning of Lab Sessions Personal Assistant Intelligent Room Project



Tim Berners Lee’s example The entertainment system was belting out the

Beatles' "We Can Work It Out" when the phone rang. When Pete answered, his phone turned the sound down by sending a message to all the other local devices that had a volume control. His sister, Lucy, was on the line from the doctor's office: "Mom needs to see a specialist and then has to have a series of physical therapy sessions. Biweekly or something. I'm going to have my agent set up the appointments." Pete immediately agreed to share the chauffeuring.



Tim Berners Lee’s example At the doctor's office, Lucy instructed her

Semantic Web agent through her handheld Web browser. The agent promptly retrieved information about Mom's prescribed treatment from the doctor's agent, looked up several lists of providers, and checked for the ones in-plan for Mom's insurance within a 20-mile radius of her home and with a rating of excellent or very good on trusted rating services. It then began trying to find a match between available appointment times (supplied by the agents of individual providers through their Web sites) and Pete's and Lucy's busy schedules. (The emphasized keywords indicate terms whose semantics, or meaning, were defined for the agent through the Semantic Web.)



Tim Berners Lee’s example In a few minutes the agent presented them

with a plan. Pete didn't like it—University Hospital was all the way across town from Mom's place, and he'd be driving back in the middle of rush hour. He set his own agent to redo the search with stricter preferences about location and time. Lucy's agent, having complete trust in Pete's agent in the context of the present task, automatically assisted by supplying access certificates and shortcuts to the data it had already sorted through.



Tim Berners Lee’s example Almost instantly the new plan was presented:

a much closer clinic and earlier times—but there were two warning notes. First, Pete would have to reschedule a couple of his less important appointments. He checked what they were—not a problem. The other was something about the insurance company's list failing to include this provider under physical therapists: "Service type and insurance plan status securely verified by other means," the agent reassured him. "(Details?)"



Tim Berners Lee’s example Lucy registered her assent at about the same

moment Pete was muttering, "Spare me the details," and it was all set. (Of course, Pete couldn't resist the details and later that night had his agent explain how it had found that provider even though it wasn't on the proper list.)



Agents in a route planning application The problem:

Mobile nurses travel from patient to patient during the day.

They have to meet some patients within certain time windows.

They have to finish in a fixed number of hours

They want to be near their home at lunch time, do not like certain patients,…



Agents in a route planning application The procedure :

The dispatching center calculates the routes for the day.

They try to minimise the travel time and to equalise the workload over the routes

The nurses are assigned a route according to some criteria.

The nurses may negotiate and exchange routes



Agents in a route planning application Agents?

Agents representing the nurses effectively do the negotiation.

They use a measure for sympathy reflecting the agent’s interrelationships

This sympathy allows for some memory in the system



Agents in a route planning application Negotiation :

Agents switch between 3 states: Enquiring

Agents with a non satisfactory route (personal cost > 30)

Listening Agents with a satisfactory route (personal cost

<= 30) Occupied

Agents involved in a discussion



Sympathy In the cause of a negotiaion, an agent

may accept a worsening of his own loopcost

It calculates cost = loopcost – sympathy/2 Cost outside [0,80] is rejected In [0,80] chance of rejection is cost/81 In case of acception, the cost is send to

the requesting agent



Sympathy The accepting agent accepts if the cost is

less than 20, or else with a probability of 1-(cost-19)/61

In case of acception, the requesting agent augments his sympathy for the offering agent with cost, and vice versa for the offering agent.

Negotiation stops when all agents are in the listening state.



Fig. 3Diagram of the algorithms used to solve the mobile nursing service problem

Generating trajectories

Clarke-Wright

Tabu Search

Assigning trajectories to nurses

Draft assignment

Negotiations

Trajectory swaps lead to sympathy level changes

Fixed trajectories

Result

Trajectories - Nurses

Patient data

Agent data



Tele Truck Two levels of scheduling using agents in

transportation1. Shipment contracting between firms2. Effectively planning the transport using

trucks Tele Truck mainly concentrated on the

second issue Trucks consist of a driver, a carrier and an

engine They may be on different places and must

be brought together for a certain job Tele truck uses a bidding scheme based on

the contract net protocol (CNP)



Personal assistant A personal assistant is a software agent

following your actions and trying to help It can follow your surfing behaviour and

decide to track certain pages for you. It can read over your shoulder and try to

find related documents on your hard disk. It can be a helping paperclip. It can follow links on the website you are

reading and point at nearby sites of interest to you.



Personal assistant In order to perform well, such an

agent must Act autonomously Be able to learn Be able to build your profile Techniques of AI must be used for

learning Datamining can reveal patterns in your

behaviour



Intelligent Room Project Room behaves as a person Reasons about what happens in the

room Tries to anticipate Has a lot of sensorial inputs Is equiped with gigantic computer

power http://www.ai.mit.edu/people/mhcoen

/



Reference “An Introduction to Multiagent

Systems”, Michael Wooldridge, Department of Computer Science, University of Liverpool, UK, John Wiley & Sons, LTD, 2002 ISBN 0-471-49691-X.

Links



Links http://allserv.kahosl.be/~patdc/Age

nts/ http://www.csc.liv.ac.uk/~mjw http://www.csc.liv.ac.uk/~mjw

/links/



Introduction to Agent Based Systems

What Vision Viewpoints Criticising MAS



What Five trends have dominated the

history of computer Ubiquity Interconnection Intelligence Delegation Human-orientation



Ubiquity (Allgegenwärtigkeit) The decreasing cost of computerpower

allows to introduce it in unexpected environments. Electrical devices Bordcomputers Mobiles …

Vb. http://ingenieur.kahosl.be/projecten/amobe/



Interconnectivity Computers are networked (Internet) Distributed systems are no longer

considered strange beasts of rare species, hard to handle and understand, not available for human control.

Nowadays we have to think of interaction as the fundamental force of computerscienc



Intelligence The complexity of the task that we

trust a computer is increasing every day.

Our ability to build trustworthy systems that can operate in critical situations increases.



“The A380, which will seat 555 passengers in a typical three-class interior layout, will enter airline service in 2006.”



Delegation As a consequence, we trust ever

more complex tasks to the computer (navigate an airplane, play at the stock market…)

Computersystems reach a level of control over humans and society before only heard of in science fiction stories.



Human-orientation The first computers were programmed

through switches. One had to understand all details of the machine to be able to use it.

Afterwards, the textual interfaces allowed to interface with the computer on a line per line basis.

From 1980 we have seen graphical usere interfaces appering. The user can manipulate objects such as files, programs, devices through their icons.



Mark 1 Colossus (Christmas 1943)



Paper thin screens The manipulation paradigm



Mission The fundamental mission for software

developers is: How do we incorporate those trends in

our applications. E.g.

Ubiquity and interconnecion: “global computing”, 1010 processors?!

Delegation: how to build devices that can take on our tasks in our place?

…



Multi agent systems An agent is a computersystem that is

able to function as a representant of its owner.

An agent can find out what it needs to realise its design goals.

A multi agent system consists of communicating agents.

Those agents will represent owners with deiverse interests and goals. They will have to collaborate, co-ordinate and negotiate.



Multi agent systems: the problem

How do we build agents that are capable to function independently and autonomously in order to perform their tasks? (agent design)

How do we build agents that are able to interact with other agents to successfully perform their tasks, especially in the case that the agents do not share interests and goals? (society design)



Questions How can collaboration emerge in

societies of self-interested agents? Which languages can agents use in their

communication? How do self-interested agents find out

when their goals are conflicting and how can the reach agreement?

Hoe do autonomous agents co-ordinate activities?



Vision Scenario 1

Due to an unexpected system failure, a space probe approaching Saturn looses contact with its Earth-based ground crew and becomes disoriented. Rather than simply disapperaing into the void, the probe recognizes that there has been a key system failure, diagnoses and isolates the fault, and correctly re-orients itself in order to make contact with its ground crew.



Autonomous vehicles



Vision Scenario 2

A key air-traffic control system at the main airport of Ruritania suddenly fails, leaving flights in the vicinity of the airport with no air-traffic control support. Fortunately, autonomous air-trafiic control systems in nearby airports recognize the failure of their peer, and cooperate to track and deal with all affected flights. The potentially disastrous situation passes without incident.



Vision Scenario 3

After the wettest and coldest (UK) winter on record, you are in desparate need of a last minute holiday somewhere warm and dry. After specifying your requirements to your personal digital assistant (PDA), it converses with a number of different Web sites, which sell services such as flights, hotel rooms, and hire cars. After hard negotiation on your behalf with a range of sites, your PDA presents you with a package holiday.



Some views of the field Agents as a paradigm for software

engineering. Agents as a tool for understanding

human societies.



Agents as a paradigm for software engineering. Interaction is the key. Programs

that proces a specific input and produce a specified output, are a minority.

In recent years, tools have been designed and developed to build systems of interacting components.



Agents as a tool for understanding human societies. “Psychohistory” allows sociological

predictions (Azimov) Socilogists can use MAS to build

simulations. (E.g. : How did social complexity evolve in the Paleolithicum?)



Objections to MAS Just distributed/concurrent

programming? Just artificial intelligence? Just game theory? Just social science?



Distributes/concurrent programming?

Important work has been done in this field since the 70’s. Agenten build on this work and add a dimension: Autonomousness: synchronisation

mechanisms are not hard coded. Encounters have an economical meaning

because of the self interested property of the agents. This differes from a situation where components are build to co-operate.



Artificial intelligence? Agents are sometimes considered as a

division of AI. or vice-versa: AI has concentrated on learning, planning,

understanding…, an agent integrates these parts to arrive at decisions. Most of the agents (99%) use conventional programming and do not incorporate any AI at all.

The social aspect has not been investigated in AI at all. It is an essential constituent of any solution build on agents. It distinguishes the human kind from its peer creatures, the annimals.



Game theory? The very same pioneers that founded

computer science created game theory and artifical intelligence: von Neumann, Turing.

Game theory is widely applied within MAS, but: The methods of game theory result in techniques

and concepts. MAS use those. The rational agent from game theory may not

have any meaning at all in the real world. The purely self-interested agents cannot contribute sufficiently toe social wellbeing even to warrant survival.



Social science? The domain offers possibilities to

sociology to eperiment. But agent systems are not at all

comparable to real world societies when complexity is at stake.



Questions on scenario 3 How do you specify your

preferences? How does the agent compare the

different offers? Which algorithms govern the

negotiations?



AIMA An agent is anything that can be

viewed as perceiving its environment through sensors and acting upon that environment through effectors

(Russel and Norvig, 1995)



Maes Software agents differ from

conventional software in that they are long-lived, semi-autonomous, proactive, and adaptive.

http://agents.www.media.mit.edu/groups/agents/



KidSim Let us define an agent as a persistent software

entity dedicated to a specific purpose. "Persistent" distinguishes agents from subroutines; agents have their own ideas about how to accomplish tasks, their own agendas. "Specific purpose" distinguishes them from entire multifunction applications; agents are typically much smaller.

http://www.dnai.com/~cypher/Publications/CACM/KidSimCACM.html



Wooldridge and Jennings Perhaps the most general way in

which the term agent is used is to denote a hardware or (more ususally) software-based computer system that enjoys the following properties: Autonomy, social ability, reactivity, pro-

activeness http://www.csc.liv.ac.uk/~mjw/



Further Examples Agents controlling production cells Agents for consistency checking in

a distributed database system Agents for assitence in programme

previewing Agents in a framework for power

controll



Agents controlling production cells

Production LineCel Cel Cel Cel

Business Logic

Manufacturing Execution System

A A A A



Agents for consistency checking in a distributed database system

Data Source 1 Data Source 2 Data Source 3 Data Source n

InformationAgent 1

InformationAgent 2

InformationAgent m

TaskAgent 1

TaskAgent j

InterfaceAgent 1

User 1 User 2 User i

InterfaceAgent 2

InterfaceAgent k

Goal and TaskSpecifications

Task

Demand forInformation/

Answer

Question/Answer

InformationIntegration

TaskProposedSolution

Results

Conflict Resolution

CollaborativeDemand Processing



ViewingRoom

Agents for assitence in programme previewing

Broadcast Planning And Administration System

Viewers PDA

ViewAgent

XML

Constraint 1

Constraint 2

A



Agents in a framework for power controll Peek levels determine the energy cost Management system allows for leveling Measuring components can be accessed

over an intranet connection They can be programmed at a low level Use agents to model the framework



PropertiesRe-active On external asynchronous

stimuli

Autonomous Controls own actions

Goal Directed Takes initiatives

Persistent Is “living”, not “running”

Communicative With other agents and humans

Learning Adaptiveness based on experience

Mobile Moves among machines in network



Why Agents

Load reduction Trusted Agent travels between server and client, carries safe protocol

Delay independence

Agents perform in real time, not over uncertain networks

Protocols Agents encapsulate protocols, for better adaptability and maintainability

Asynchronousness

Togetter with autonomy, agents can act on their own, without necessity for continuous interaction



Why Agents (cont)

Adaptivity Agents react autonomously on changes in the environment

Heterogenity Distributed systems are heterogeneous in nature. Agents are optimal for system integration

Robustness Mobile agents can make a distributed system more robust and fault tolerant by deciding autonomously in case of an error



Agents as a Network Computing Paradigm Client Server

Code-on-demand

Client

Server

Know How Data

Client

Know How

Know How

Data

Server



Agents as a Network Computing Paradigm (cont) Mobile Agents

Agent

Know How

Host

Agent

Know How

Host

NwNetwork Network



Multi Agent Systems (MAS) Standalone agents incorporate all

functionalities of an agent. Multi Agent systems define co-

operating agents for tasks of which an individual agent is not capable

Intelligence is an emerging property The Internet is a driving factor



Multi Agent Systems (cont) Aspects of research

Aggregation, Communication, Co-ordination, Collaboration, Negotiation

Helping agents (Middle agents) Facilitator, Mediator, Broker,

Matchmaker, Blackboard Agent interaction

Language, Format, Ontology



Where to apply User interface

Personal representative for the user, Metaphorically enforced image of functionality

Distributed systems Asynchronicity, autonomy, unreliable

communication Algorithmic paradigm

E.g. optimization problem, agents incorporate separate strategies

E.g. heat distribution in a plate



Where to apply (cont) Software engineering

Agents abstract communication, control, decision taking, autonomy, persistence

Enforce encapsulation, modularity, re-usability, concurrency, distributed operation



Pitfalls Agents solve everything Buzzwords are concepts Forget the main objective: software Multi agent systems solve all problems

in distributed computing Related technology is irrelevant Forget concurrency Build your own architecture We have a general architecture



Pitfalls Agents use too much ai Agents use too little ai You see agents everywhere You have too many agents You have too little agents You spend all your time on an infrastructure Your system is disorganised Let us start from a tabula rasa Forget about de facto standards

do software agents know what they talk about?

Documents

doctors agent

petes agent

lucys agent

semantic web agent

software agents

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berners lees examplealmost

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