Applications of Agents Agent characteristics Agent architecture Summary CM30174 + CM50206 Introduction to Intelligent Agents Marina De Vos, Julian Padget Introduction / version 0.4 October 3, 2011 De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 1 / 35
Applications of Agents Agent characteristics Agent architecture Summary Authors/Credits for this lecture “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 3, 2011 2 / 35
Applications of Agents Agent characteristics Agent architecture Summary Content Applications of Agents 1 Agent characteristics 2 Agent architecture 3 Summary 4 De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 3 / 35
Applications of Agents Agent characteristics Agent architecture Summary Motivation Agents might help solve some difficult 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 3, 2011 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 Agent architecture 3 Summary 4 De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 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: AGENT sense act ENVIRONMENT The situated agent. De Vos/Padget (Bath/CS) CM30174/Intro October 3, 2011 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 3, 2011 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 3, 2011 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 3, 2011 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 3, 2011 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 3, 2011 11 / 35
Applications of Agents Are agents new or different? Agent characteristics Agents and their environment Agent architecture The intentional perspective Summary Social Ability The real world is a multi-agent environment: we cannot go around attempting to achieve goals without taking others into account. Some goals can only be achieved with the cooperation of others. Suggests need for: Information/models of other agents’ state Trust metrics Reputation models (e.g. FOAF) Social ability in agents is the 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 3, 2011 12 / 35
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