ann nowe vub 1 what are agents anyway?. ann nowe vub 2 overview agents agent environments...

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  • Ann NoweVUB *What are agents anyway?

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  • Ann NoweVUB *OverviewAgentsAgent environmentsIntelligent agentsAgents versus objects

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  • Ann NoweVUB *Agent Wooldridge & JenningsA computer system that is situated in some environment and is capable of autonomous action in its environment to meet its design objectives.

    Learning?

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  • Ann NoweVUB *Agent Russell & NorvigAn agent is anything that can be viewed asPerceiving its environmentActing upon that environment

    What is the difference between the two definitions???

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  • Ann NoweVUB *Autonomyagents operate without the direct intervention of humans or other agents, and have some kind of control over their actions and internal state

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  • Ann NoweVUB *Agent and EnvironmentAgentEnvironmentSensorsEffectorsautonomousprocessingActionOutputSensorInput

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  • Ann NoweVUB *Intelligent AgentsIntelligent agents require flexible autonomous actionRequires the following attributesreactivitypro-activitysocial ability

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  • Ann NoweVUB *Reactiveagents perceive their environment and respond, in a timely fashion, to changes that occur in it

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  • Ann NoweVUB *Pro-activeagents do not simply react to their environment, they are able to exhibit goal-directed behavior by taking the initiativeKey is a balance between goal-directedand reactive behavior!

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  • Ann NoweVUB *Socialagents interact with other agents, and possibly humans, via an agent-communication languageSocial ability implies coordinationcooperation versus competition

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  • Ann NoweVUB *Example/Non-ExampleInformation seeking agentThe user tells the agent what type of information is desired.The agent goes to known web sites, databases, and other sources (including other agents) to collect information about the desired subject.After collecting the data, the agent fuses the information into a succinct report and returns it to the user.

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  • Ann NoweVUB *Example/Non-ExamplePrint agentThe user submits documents to the agent and tells it what printer to use.The agent takes the users document, as well as documents from other users, orders them by size and prints them one by one.The agent returns the current status of the document and printer when asked by the user.The agent tells the user when the document has been printed.

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  • Ann NoweVUB *Example/Non-ExampleContract Manager agentThe user gives the agent a task to performThe manager agent then sends the task to a group of contractor agents for bidIf the contractor agents want to bid, they submit a bid on the taskThe manager agent waits for a time and then selects the best bid submitted by potential contractor agentsUpon completion, the manager agent notifies the user

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  • Ann NoweVUB *Agents vs. ObjectsArent agents just like objects?YESComputational entitiesEncapsulate statePerform actionsCommunicate via message passing

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  • Ann NoweVUB *Agents vs. ObjectsArent agents just like objects?NOAgents embody a strong notion of autonomyAgents are capable of flexible behaviorAgents operate in their own thread of controlObjects : invoke Agents : request

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  • Ann NoweVUB *Agents vs. ObjectsCant you build OO systems with these characteristics?Yes, but you have built a multiagent system using object-oriented tools and techniques!That makes agents a specialization of the general notion of an objectAn active object is one that encompasses its own thread of control []. Active objects are generally autonomous, meaning that they can exhibit some behavior without being operated upon by another object. Passive objects on the other hand, can only undergo a state change when explicitly acted upon. G. Booch

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  • Ann NoweVUB *Programming progressionProgramming has progressed through: machine codeassembly languagemachine-independent programming languagessub-routinesprocedures & functionsabstract data-typesobjectsto agents

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  • Ann NoweVUB *Agents vs. Expert systemsExpert systems do not directly act on environments, act as consultants.input not via sensors, but via users, who act as middle mancooperation? maybe backboards

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  • Ann NoweVUB *SummaryAgents are capable of autonomous action in its environment to meet its design objectivesIntelligent agents require flexible autonomous action to meet its objectivesAgents are a specialization of objects that are autonomous, flexible, and operate in their own thread of control

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  • Ann NoweVUB *Agent Architectures

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  • Ann NoweVUB *OverviewAbstract ArchitecturesState, Action, PerceptionReflexive agents, Agents with stateConcrete ArchitecturesLogicReactiveBelief-Desire-IntentionLayered

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  • Ann NoweVUB *Abstract ArchitecturesFormalize abstract view

    S = {s1, s2, } environment states

    A = {a1, a2, } set of possible actionsAllows us to view an agent as a functionaction : S* AInteractions of agent and environment history a1 a2 a3 a4 an an+1 h : s0 s1 s2 s3 sn

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  • Ann NoweVUB *Abstract ArchitectureactionEnvironmentactionsstatesactionenv : S x A (S)action : S* A

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  • Ann NoweVUB *Perceiving StateAgent cant see state directlyRequires perception

    see : S* P

    And action is actually based on perceptions, not state

    action : P* A

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  • Ann NoweVUB *Perceiving StateEnvironmentactionsstatesseeaction

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  • Ann NoweVUB *Reflexive agents decide what to do without regard to history purely reflexiveaction : P A

    Example - thermostat

    off if temp = okaction(s) = on otherwise

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  • Ann NoweVUB *Agents with StateHow do we represent the history of percepts?Sequence of perceptions is unintuitive and computationally inefficientAn agent can maintain its perception of the current state of the environmentEquivalent to history of perceptionsRequires more machinery

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  • Ann NoweVUB *Abstract StatesRepresent new construct - stateI = {i1, i2, i3 } set of internal statesWe now map internal state to actionsaction : I AUpdate internal statenext : I x P I

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  • Ann NoweVUB *Agents with StateEnvironmentactionsstatesseeactionnextstate

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  • Ann NoweVUB *Concrete ArchitecturesLogic-Based ArchitecturesReflexive ArchitecturesBelief-Desires-Intentions ArchitecturesLayered Architectures

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  • Ann NoweVUB *See and Next FunctionsSee and next function stay basically the samesee : S Pnext : D x P D

    Internal state = set of logical expressions

    e.g. In(x,y) Dirt(x,y) Facing(d)What the agent believes is true

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  • Ann NoweVUB *Model decision making by a set of deduction rules for inferenceaction : D A

    Use logical deduction to try to prove the next action to take, given the current state. If no action can be proven, select an action that is consistent with the rules and database Logical Decision MakingIn(x,y) &Dirt(x,y) -> Do(suck) In(0,0) & notDirt(0,0) & Facing(north) -> Do(forward)In(0,1) & notDirt(0,1) & Facing(north) -> Do(forward)In(0,2) & notDirt(0,2) & Facing(north) -> Do(turn_right)

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  • Ann NoweVUB *ProSimple, elegant, logical semanticsConComputational complexityRepresenting the real worldnot applicable in (fast) changing environments

    Logical Agency Summary

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  • Ann NoweVUB *Reactive ArchitecturesReactive Architectures do NOT usesymbolic world modelsymbolic reasoningThree key ideas of reactive agents (Brooks)raw sensor data - representation of data is close to sensor data, not symbolic (physical-symbol grounding hypothesis)emergent functionality - no a priori specification of behaviortask decomposition - composed of a collection of autonomous modules, each responsible for a single task

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  • Ann NoweVUB *BrooksSubsumption ArchitectureExploreWanderAvoid ObstaclesSensingActingOriginally developed to control RobotsHierarchy of tasksTasks compete to exercise controlLower levels represent more primitive behaviorsLower levels have precedence

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  • Ann NoweVUB *Subsumption architectuurA set of behaviors, one for each task.A behavior is:situation action rule, ora neural network, or ... A direct link between situation and action.An ordering on behaviors.The subsumption hierarchies.Lower levels inhibit higher levels.

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  • Ann NoweVUB *Layered behaviorssensorsbehavior 0behavior 1behavior 2::actionsss

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  • Ann NoweVUB *LimitationsSuppose the robot wants to go to the goal. Can we do this with the subsumption architecture?

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    emergent behaviorPros & ConsProsSimplicity, computationally tractable, robust, eleganceConsSufficiency of local information is neededNo accounting for non-local/long term effectsHow to do learning

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  • Ann NoweVUB *Belief-Desires-IntentionsRooted in practical reasoningDeciding what goals to achieveDeciding how to achieve those goalsBDIBeliefs = current stateD