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Do software agents know what they talk about? Agents and Ontology dr. Patrick De Causmaecker, Nottingham, March 7-11, 2005 Overview Agents Ontology Communication RDF Semantic Web Sample Implementations Nottingham, March 2005 Agents and Ontology [email protected] 2 Agents Examples Definitions Properties Why Agents Pitfalls Models Architectures Standards Nottingham, March 2005 Agents and Ontology [email protected] 3 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 Nottingham, March 2005 Agents and Ontology [email protected] 4 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. Nottingham, March 2005 Agents and Ontology [email protected] 5 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.) Nottingham, March 2005 Agents and Ontology [email protected] 6 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. Nottingham, March 2005 Agents and Ontology [email protected] 7 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?)" Nottingham, March 2005 Agents and Ontology [email protected] 8 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.) Nottingham, March 2005 Agents and Ontology [email protected] 9 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,… Nottingham, March 2005 Agents and Ontology [email protected] 10 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 Nottingham, March 2005 Agents and Ontology [email protected] 11 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 Nottingham, March 2005 Agents and Ontology [email protected] 12 Agents in a route planning application Negotiation : Agents switch between 3 states: Enquiring Listening Agents with a non satisfactory route (personal cost > 30) Agents with a satisfactory route (personal cost <= 30) Occupied Nottingham, March 2005 Agents involved in a discussion Agents and Ontology [email protected] 13 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 Nottingham, March 2005 Agents and Ontology [email protected] 14 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. Nottingham, March 2005 Agents and Ontology [email protected] 15 Clarke-Wright Patient data Generating trajectories Tabu Search Fixed trajectories Draft assignment Assigning trajectories to nurses Negotiations Trajectory swaps lead to sympathy level changes Agent data Result Trajectories - Nurses Fig. 3Diagram of the algorithms used to solve the mobile nursing service problem Nottingham, March 2005 Agents and Ontology [email protected] 16 Tele Truck Two levels of scheduling using agents in transportation 1. 2. Shipment contracting between firms 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) Nottingham, March 2005 Agents and Ontology [email protected] 17 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. Nottingham, March 2005 Agents and Ontology [email protected] 18 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 Nottingham, March 2005 Agents and Ontology [email protected] 19 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/ Nottingham, March 2005 Agents and Ontology [email protected] 20 Reference “An Introduction to Multiagent Systems”, Michael Wooldridge, Department of Computer Science, University of Liverpool, UK, John Wiley & Sons, LTD, 2002 ISBN 0-471-49691X. Links Nottingham, March 2005 Agents and Ontology [email protected] 21 Links http://allserv.kahosl.be/~patdc/Agents/ http://www.csc.liv.ac.uk/~mjw http://www.csc.liv.ac.uk/~mjw/links/ Nottingham, March 2005 Agents and Ontology [email protected] 22 Introduction to Agent Based Systems What Vision Viewpoints Criticising MAS Nottingham, March 2005 Agents and Ontology [email protected] 23 What Five trends have dominated the history of computer Ubiquity Interconnection Intelligence Delegation Human-orientation Nottingham, March 2005 Agents and Ontology [email protected] 24 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/ Nottingham, March 2005 Agents and Ontology [email protected] 25 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 Nottingham, March 2005 Agents and Ontology [email protected] 26 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. Nottingham, March 2005 Agents and Ontology [email protected] 27 “The A380, which will seat 555 passengers in a typical three-class interior layout, will enter airline service in 2006.” Nottingham, March 2005 Agents and Ontology [email protected] 28 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. Nottingham, March 2005 Agents and Ontology [email protected] 29 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. Nottingham, March 2005 Agents and Ontology [email protected] 30 Mark 1 Colossus (Christmas 1943) Nottingham, March 2005 Agents and Ontology [email protected] 31 Paper thin screens Nottingham, March 2005 The manipulation paradigm Agents and Ontology [email protected] 32 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? … Nottingham, March 2005 Agents and Ontology [email protected] 33 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. Nottingham, March 2005 Agents and Ontology [email protected] 34 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) Nottingham, March 2005 Agents and Ontology [email protected] 35 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? Nottingham, March 2005 Agents and Ontology [email protected] 36 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. Nottingham, March 2005 Agents and Ontology [email protected] 37 Autonomous vehicles Nottingham, March 2005 Agents and Ontology [email protected] 38 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. Nottingham, March 2005 Agents and Ontology [email protected] 39 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. Nottingham, March 2005 Agents and Ontology [email protected] 40 Some views of the field Agents as a paradigm for software engineering. Agents as a tool for understanding human societies. Nottingham, March 2005 Agents and Ontology [email protected] 41 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. Nottingham, March 2005 Agents and Ontology [email protected] 42 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?) Nottingham, March 2005 Agents and Ontology [email protected] 43 Objections to MAS Just distributed/concurrent programming? Just artificial intelligence? Just game theory? Just social science? Nottingham, March 2005 Agents and Ontology [email protected] 44 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. Nottingham, March 2005 Agents and Ontology [email protected] 45 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. Nottingham, March 2005 Agents and Ontology [email protected] 46 Nottingham, March 2005 Agents and Ontology [email protected] 47 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 Nottingham,survival. March 2005 Agents and Ontology [email protected] 48 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. Nottingham, March 2005 Agents and Ontology [email protected] 49 Questions on scenario 3 How do you specify your preferences? How does the agent compare the different offers? Which algorithms govern the negotiations? Nottingham, March 2005 Agents and Ontology [email protected] 50 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) Nottingham, March 2005 Agents and Ontology [email protected] 51 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/ Nottingham, March 2005 Agents and Ontology [email protected] 52 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 Nottingham, March 2005 Agents and Ontology [email protected] 53 Wooldridge and Jennings Perhaps the most general way in which the term agent is used is to denote a hardware or (more ususally) softwarebased computer system that enjoys the following properties: Autonomy, social ability, reactivity, proactiveness http://www.csc.liv.ac.uk/~mjw/ Nottingham, March 2005 Agents and Ontology [email protected] 54 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 Nottingham, March 2005 Agents and Ontology [email protected] 55 Agents controlling production cells Business Logic Manufacturing Execution System A A A A Cel Cel Cel Cel Production Line Nottingham, March 2005 Agents and Ontology [email protected] 56 Agents for consistency checking in a distributed database system User 1 Goal and Task Specifications User 2 User i Interface Agent 2 Interface Agent k Results Interface Agent 1 Task Task Agent 1 Demand for Information/ Answer Information Agent 1 Task Conflict Resolution Proposed Solution Task Agent j Information Integration Collaborative Demand Processing Information Agent 2 Data Source 2 Data Source 3 Information Agent m Question/ Answer Data Source 1 Nottingham, March 2005 Data Source n Agents and Ontology [email protected] 57 Agents for assitence in programme previewing Viewers PDA Constraint 1 Constraint 2 Broadcast Planning And Administration System Viewing Room A Nottingham, March 2005 XML View Agent Agents and Ontology [email protected] 58 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 Nottingham, March 2005 Agents and Ontology [email protected] 59 Properties Re-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 Nottingham, March 2005 Agents and Ontology [email protected] 60 Why Agents Load reduction Delay independence Protocols Asynchronousness Nottingham, March 2005 Trusted Agent travels between server and client, carries safe protocol Agents perform in real time, not over uncertain networks Agents encapsulate protocols, for better adaptability and maintainability Togetter with autonomy, agents can act on their own, without necessity for continuous interaction Agents and Ontology [email protected] 61 Why Agents (cont) Adaptivity Heterogenity Robustness Nottingham, March 2005 Agents react autonomously on changes in the environment Distributed systems are heterogeneous in nature. Agents are optimal for system integration Mobile agents can make a distributed system more robust and fault tolerant by deciding autonomously in case of an error Agents and Ontology [email protected] 62 Agents as a Network Computing Paradigm Client Server Client Server Data Know How Code-on-demand Client Know How Nottingham, March 2005 Know How Server Data Agents and Ontology [email protected] 63 Agents as a Network Computing Paradigm (cont) Mobile Agents Agent Network Nw Agent Know How Know How Host Host Nottingham, March 2005 Agents and Ontology [email protected] Network 64 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 Nottingham, March 2005 Agents and Ontology [email protected] 65 Multi Agent Systems (cont) Aspects of research Helping agents (Middle agents) Aggregation, Communication, Coordination, Collaboration, Negotiation Facilitator, Mediator, Broker, Matchmaker, Blackboard Agent interaction Language, Format, Ontology Nottingham, March 2005 Agents and Ontology [email protected] 66 Where to apply User interface Distributed systems Personal representative for the user, Metaphorically enforced image of functionality Asynchronicity, autonomy, unreliable communication Algorithmic paradigm E.g. optimization problem, agents incorporate separate strategies E.g. heat distribution in a plate Nottingham, March 2005 Agents and Ontology [email protected] 67 Where to apply (cont) Software engineering Agents abstract communication, control, decision taking, autonomy, persistence Enforce encapsulation, modularity, reusability, concurrency, distributed operation Nottingham, March 2005 Agents and Ontology [email protected] 68 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 Nottingham, March 2005 Agents and Ontology [email protected] 69 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 Nottingham, March 2005 Agents and Ontology [email protected] 70