Applications of Agents Agent characteristics Agent architecture Summary CM30174 + CM50206 Introduction to Intelligent Agents Semester 1, 2009-10 Marina De Vos, Julian Padget Introduction / version 0.4 October 5, 2010 De Vos/Padget (Bath/CS) CM30174/Intro October 5, 2010 1 / 35 Applications of Agents Agent characteristics Agent architecture Summary Authors/Credits for this lecture Material sourced from Michael Wooldridge’s book “An Introduction to Multiagent Systems”, Chapters 1 and 2 [Wooldridge, 2009]. Agentlink material supplied by Mike Luck. De Vos/Padget (Bath/CS) CM30174/Intro October 5, 2010 2 / 35 Applications of Agents Agent characteristics Agent architecture Summary Content Applications of Agents 1 Agent characteristics 2 Agent architecture 3 4 Summary De Vos/Padget (Bath/CS) CM30174/Intro October 5, 2010 3 / 35
Applications of Agents Agent characteristics Agent architecture Summary Motivation Agents might help solve some hard problems BUT they also create new ones: Independent action ⇒ responsibility, but whose? for what? How to engineer reliable MAS? A new challenge for SE? Software to cooperate, coordinate, negotiate, adapt, argue Application areas? Here are some examples collected by the Agentlink network De Vos/Padget (Bath/CS) CM30174/Intro October 5, 2010 4 / 35 Monday, 14 June 2010 Production scheduling optimisation • Simulation and optimisation of processes in a corrugated box plant • Objective: to find the production schedule that allows stock level reduction without compromising delivery times • Plant modelling is complex (modelling relationships between customer order patterns, factory capacity, machine speeds, order batching and warehouse size, etc.) • Combines agent technology with discrete event simulation • Used as simulation tool – it helped choose between two customers, by determining the necessary plant capacity and the incurred costs of serving these customers • SCA Packaging reduced inventory levels by 35% while maintaining delivery commitments 4 www.dcs.kcl.ac.uk/sta f /mml Monday, 14 June 2010
Monday, 14 June 2010 Vessel transportation scheduling • Ocean i-Scheduler, developed by Magenta Technology for Tankers International • Finds the most profitable allocation of cargoes to vessels (oil carriers) for a fleet • Agents model vessels and cooperate with each other to find the optimal schedule for the entire fleet • Schedules are adapted in real-time in response to changes in the environment, e.g.: • cargoes change constantly, • tankers can fail unexpectedly, • oil transportation costs change daily 6 www.dcs.kcl.ac.uk/sta f /mml Monday, 14 June 2010 Monday, 14 June 2010
Supply Chain Production Optimiser • NuTech; Client: Air Liquide America • Optimisation of production and distribution of liquefied gases • Combines domain dependent heuristics, with genetic algorithms and ant based optimisation: • the genetic algorithm optimises the production schedule at each plant • the ant algorithm optimises energy distribution routes from plant to customer • Solutions are adapted dynamically to take into account fluctuations in energy prices, weather changes, client demand and desired inventory levels • Information is fed back into the control systems that operate the power plant 8 www.dcs.kcl.ac.uk/sta f /mml Monday, 14 June 2010 Monday, 14 June 2010 Human Variability in Computer Generated Forces • Agent Oriented Software for the UK MoD • Simulation of combat situations for military training • Models the influence of moderating factors (e.g. fatigue, ca f eine intake) on soldiers’ behaviour, both at individual and team levels • Built on Jack Intelligent Agents toolkit, makes use of the BDI reasoning model • Integrated with other simulation environments (CGF systems) used by the MoD 10 www.dcs.kcl.ac.uk/sta f /mml Monday, 14 June 2010
Monday, 14 June 2010 Aerogility • Software agents represent the Aftermarket resources - people, assets and processes. • For each resource we capture their purpose, business goals and objectives. • The interactions between the agents - Aftermarket resources - are determined by easily changed parameters covering overall strategies, management policies and organisation configurations, as well as business processes and rules. • The overall Aftermarket model yields SLA, KPI and operating metrics. 12 www.dcs.kcl.ac.uk/sta f /mml Monday, 14 June 2010 Applications of Agents Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Content Applications of Agents 1 Agent characteristics 2 Are agents new or different? Agents and their environment The intentional perspective 3 Agent architecture Summary 4 De Vos/Padget (Bath/CS) CM30174/Intro October 5, 2010 5 / 35
Applications of Agents Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary What is an Agent? An intelligent agent is a computer system capable of flexible, autonomous action in some environment: the situated agent. AGENT sense act ENVIRONMENT De Vos/Padget (Bath/CS) CM30174/Intro October 5, 2010 6 / 35 Applications of Agents Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary What are Multi -Agent Systems? An agent can be more useful in the context of others: Can concentrate on tasks within competence Can delegate other tasks Can use ability to communicate, coordinate, negotiate How to organize? AGENT 2 AGENT 1 AGENT 3 sense act act sense sense act ENVIRONMENT De Vos/Padget (Bath/CS) CM30174/Intro October 5, 2010 7 / 35 Applications of Agents Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agent Characteristics Major: Reactive: has an on-going interaction with its environment, and responds to changes that occur in it (in time for the response to be useful). Pro-active: means generating and attempting to achieve goals Social: ability to interact with other agents (and possibly humans) via some kind of agent-communication language, and perhaps cooperate with others. De Vos/Padget (Bath/CS) CM30174/Intro October 5, 2010 8 / 35
Applications of Agents Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Agent Characteristics Minor: Mobility: The ability of an agent to move around an electronic network. Veracity: Whether an agent will knowingly communicate false information. Benevolence: Whether agents have conflicting goals, and thus whether they are inherently helpful. Rationality: Whether an agent will act in order to achieve its goals, and will not deliberately act so as to prevent its goals being achieved. Learning/adaption: Whether agents improve performance over time. De Vos/Padget (Bath/CS) CM30174/Intro October 5, 2010 9 / 35 Applications of Agents Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Reactivity Simple and not-so-simple agents: thermostat washing machines engine management systems? house management system — “intelligent buildings”? If environment never changes, success or failure are meaningless — program executes blindly The real world is not like that: change, incompleteness. Many (most?) interesting environments are dynamic Software is hard to build: planning, failures, choice A reactive system interacts continuously with environment, responds to changes – consider a robot sharing an environment with people... De Vos/Padget (Bath/CS) CM30174/Intro October 5, 2010 10 / 35 Applications of Agents Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Proactivity Reactive systems are relatively easy: stimulus → response Want agents to do things for us Want goal-directed behaviour — implies AI techniques, e.g. reasoning with rules Pro-activity: generating and achieving goals Not driven (solely) by events Taking the initiative Recognising opportunities Need a model of the environment to support the decision-making process: symbolic — classical AI non-symbolic — neural networks, time series, Markov decision processes etc. De Vos/Padget (Bath/CS) CM30174/Intro October 5, 2010 11 / 35
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