Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998 Lin Padgham Lin Padgham Artificial Intelligence Group Artificial Intelligence Group Dept. of Computer Science Dept. of Computer Science RMIT RMIT Agent concepts and issues Agent concepts and issues Thanks to AOS and E. Sonenberg for some borrowed slides
Agent concepts and issues. Lin Padgham Artificial Intelligence Group Dept. of Computer Science RMIT. Thanks to AOS and E. Sonenberg for some borrowed slides. Many choices. Smart agents with complex knowledge Systems of many small simple agents Agents in the physical world (robots) - PowerPoint PPT Presentation
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Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Lin PadghamLin Padgham
Artificial Intelligence GroupArtificial Intelligence Group
Dept. of Computer ScienceDept. of Computer Science
RMITRMIT
Agent concepts and issuesAgent concepts and issues
Thanks to AOS and E. Sonenberg for some borrowed slides
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Many choicesMany choices
Smart agents with complex knowledge Systems of many small simple agents Agents in the physical world (robots) Small mobile agents ...
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Agent approachesAgent approaches
Behaviour based situated, reactive (Brooks, Maes …)
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Behaviour based architectureBehaviour based architecture
perceptsactions
behaviour layer 1
behaviour layer 2
behaviour layer 3
behaviour layer n
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Behaviour based philosophyBehaviour based philosophy
no internal world model no symbolic reasoning goals a function of observer ‘intelligence’ is emergent
environmentagent
behaviours
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Example behavioursExample behaviours
avoid obstacle
go to point
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Merging - corridor exampleMerging - corridor example
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Merging - go-around exampleMerging - go-around example
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Merging - won’t always workMerging - won’t always work
weighting
suppression
activation
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Oscillating behavioursOscillating behaviours
run to ball vs mark opponent
mark opponentrun to ball
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Behaviour based summaryBehaviour based summary
impressive robotics systems– e.g. walking insect-like robots
work well to a certain complexity
many layers and behaviours lead to difficulty in understanding and dealing with interactions.
no methodology to help with managing interactions
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Pragmatic plan-based systemsPragmatic plan-based systems
came out of symbolic reasoning and planning recognise need for reactivity accept adequate vs optimal solution recognise that world is dynamic earliest examples IRMA and PRS use pre-defined plans rather than generating plans
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
BDI basic conceptsBDI basic concepts
beliefs - local knowledge base, can be updated desires/goals - what the agent is trying to accomplish actions - elementary things the agent can do to communicate,
or change the environment plans - predetermined sequences of actions (or calls to other
plans) that can accomplish specified tasks intentions - currently “adopted” plans - multiple concurrently events - things that ‘happen’, can be internal or external
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
BDI architectureBDI architecture
beliefs /
world model
goals /
desires
intentionsplans
events
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Plan selection and executionPlan selection and execution
invocation condition/trigger - when should plan be considered
context condition - under what conditions is plan appropriate
body - actions, sub-goals, sub-plans maintenance condition - abort if broken fail procedure - may be atomic action or more complex
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Ongoing reasoningOngoing reasoning
when an event occurs, the agent– looks for a relevant plan– for each relevant plan, examines applicability– selects an applicable plan & starts executing
ongoing questions– what goal to pursue or event to react to - now !– how to pursue it– when to suspend / abandon / change– level of commitment, more/less sensitive to change
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
High level programming constructsHigh level programming constructs
Achieve– semantics of try and retry until achieved or possibilities
exhausted Test
– check if known, if not find out Wait-for
– monitor periodically while doing other things
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Illustrative exampleIllustrative example
Airport– runway– air traffic controller– multiple aircraft
taking off landing
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Further layers– plan generation– social awareness– emotion/personality– …
percepts actions
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Vacuum cleaner exampleVacuum cleaner example
vacuum cleaner problem (Firby, 1993) problem: autonomous agent (robot) vacuuming a room without
over-engineering the task solution requires a range of reactive and deliberative behaviour
– reactive: avoid obstacles - furniture, children– deliberative: map the room, plan the task, interact with
humans
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Vacuum cleaner - part 1Vacuum cleaner - part 1
simple vacuum task (vacuum as agent) – objects in room are simple (e.g., convex)– stationary during cleaning, may move between cleanings– people occasionally move through the room
reactive architecture appropriate / sufficient for navigation– minimal state, simple sensing e.g. rug, dirt– simple strategies can cover the room without a map
(e.g., random walk, slow spiral outwards, etc.)
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Vacuum cleaner - part 2Vacuum cleaner - part 2
synthetic vacuum task– complex objects (clean around, under)– moving and stationary objects treated differently– different vacuuming strategies at different times in
different situations need to represent object classes and associated strategies
– solution naturally expressed in terms of goals and plans– still need reactive capabilities for obstacle avoidance
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Vacuum cleaner - part 3Vacuum cleaner - part 3
intelligent vacuum task– negotiate with user
“Do under the sofa first.” “Do here later.” “Stay away from the baby.”
– be able to operate correctly in varied situations
need higher level understanding of goals and plans useful to retain reactive properties for obstacle avoidance
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Multi-agent systems - (DAI)Multi-agent systems - (DAI)
no global control mechanism each agent has limited knowledge/capability asynchronous computation Interaction
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
TeamworkTeamwork
multi-agent plans roles authority relationships - can one assume the other will take
on tasks when requested? global information or partial information? individual or joint goals? conflicting priorities?
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Coordination protocols, e.g.Coordination protocols, e.g.
contract net– manager / contractor multi-pass bidding system
partial global planning– agents exchange partial information on goals– use info on others’ goals to reason about own activities– use contract-net to assign tasks to underutilised agents
conversation plans– use predetermined knowledge of others’ capabilities and known
authority relationships to drive negotiation
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Communication mechanismsCommunication mechanisms
blackboards contract net protocol
– manager / contractor multi-pass bidding system interaction protocols
– e.g. request, confirm, ... shared plans
– possibly using roles joint intentions, joint goals, joint commitments
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Open environmentsOpen environments
agents need to find each other interoperability - agent communication languages middle agents
– matchmakers– brokers
agent difference
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
Agent Communication Languages Agent Communication Languages (ACLs)(ACLs)
general requirements– high level - concise, easy to parse, readable – separate communication acts from domain information– fit with modern networking technology - support
point-to-point, broadcast, multicast– be independent of transport mechanisms (http,
TCP/IP...)– support for “facilitators”
Agent-based Software Engineering Workshop Software Engineering Australia (Vic.) September 1998
KQMLKQML
a proposed “standard” for inter-agent communication, still under development
((ask-oneask-one: sender joe: sender joe: content (: content (PRICE IBM ?PRICE IBM ?
priceprice)): receiver stock-server: receiver stock-server: reply-with ibm-stock: reply-with ibm-stock: language LPROLOG: language LPROLOG: ontology NYSE-: ontology NYSE-