Transcript Agent Based Software Development

Agent Based
Software Development
Michael Luck, Ronald Ashri and
Mark d’Inverno
Agent Architectures
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What kind of computational entity is an
agent?
Why architecture?
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to predict what an agent in its current state will
do in its current environment
a methodology or blueprint for building agent
systems
Different Agent Architectures
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Single-agent (micro)
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reactive agent architectures
deliberative agent architectures
Hybrid agent architectures
Multi-agent (micro and macro)
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Macro – norms, protocols, languages
Micro – designing individual agents that can work
effectively in a society
From the Traditional AI View to
Reactive Agent Architectures
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Traditional AI View
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Symbolic (internal) representation
Manipulate this representation to work out what
to do next
More recent View
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Effective behaviour does not need representation
and manipulation
Symbolic reasoning is very resource demanding
Agents must be situated and embodied with
symbol “reflex” rules
Reactive Agent Architectures
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behaviour directly coupled with the world
stimulus-response rules
Brooks was a pioneer
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Maes also
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Subsumption Architecture
Agent network architecture
“Intelligent behaviour is an emergent
phenomenon arisiing from the interaction of
societies of nonintelligent systems’’
Subsumption Architectures
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A means of controlling behaviour of mobiles
robots in real time – 3 basic requirements
Agents that can cope with multiple goals
Agents should have multiple sensors
Agents should be robust
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Function when some sensors fail
Function in dynamic uncertain environments
Layered Architecture –
Task Achieving Behaviours
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Avoid contact with objects
Wander
Explore
Build a map and plan routes
Notice environment change
Identify objects, and perform certain object-related
tasks
Build and execute plans to change world in some
desirable way
Reason about the behaviour of other objects, and
modify plans
Subsumption Architecture
Operation
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Layers unaware of layers above them
Layers able to examine details of layers
below them
Each layer connected to all sensors but only
extracts information relevant to that layer
Layers can suppress lower layers
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suppressing inputs
inhibiting outputs
The Subsumption Architecture
Pros and Cons of the
Subsumption Architecture
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Pros
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Showed decomposition by behaviour rather than function
was possible
Modular structure and a clear control methdolology
Impact on other architectures
Cons
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No explicit reasoning – how would it scale up?
No design methodology – built on intuition rather than a
set of guiding principles
Difficult to perform any formal analysis and so predict or
explain behaviour
Agent Network Architecture
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Based on the notion that intelligent systems
consist of societies of mindless interacting
systems
Consists of competence modules
compete for behaviour using activation
Activation based on
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whether a module is currently executable
perception of the environment
current goals
Agent Network Architecture
Operation
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Modules are linked
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activated modules increase the activation of their
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Non executable models activate their
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All modules decrease activation of their conflictors
successors
predecessors
Architecture is distributed, modular and
robust.
Has not been extended for designing multiagent systems in general
Deliberative Agent Architectures
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Maintain and manipulate representations of the
world
To model rational or intentional agency use mental
attitudes
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Eg. Beliefs, goals, desires, knowledge, plans,
motivations, intentions
3 Basic categories
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Informative (about the world)
Motivational (what drives the agent)
Deliberative (directs agent behaviour)
Why Mental Attidues?
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First, if an agent can be described in terms of what
it knows, what it wants, and what it intends,
then,since it is modeled on familiar concepts, it
becomes possible for users to understand and
predict its behavior.
Second, understanding the relationship between
these different attitudes and how they affect
behavior can provide the control mechanism for
intelligent action in general.
Third, computational agents designed in this way
may be able to interpret the behavior of others
independently from any implementation.
BDI Theory and Architecture
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Supports many deliberative architectures
Belief, desire and intention (theory)
Represented as data structures (beliefs, goals
and intentions) which determine the
operation of an agent
Cannot commit to all its goals as resource
bounded so selects some which are the
agents’ intentions
Success of BDI
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Many formal models
Generic agent architectures
Basis for many previous and current agent
architectures (including MAS)
Successfully applied to more than just toy problems
or simulations
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Air traffic control
Customer-service applications (Agentis)
First BDI system known as the Proceedural Reasoning
System (PRS)
The PRS Architecture
PRS
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Beliefs, goals, intentions and plan library
Agent perceive the world through external
events
Plans – proceedural knowledge
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Recipes for action (tree labeled with actions and
formulas which evaluate to a boolean)
Trigger (what an agent must perceive)
Context (what an agent must believe)
If plans cannot proceed they post internal events
From Plans to Intentions
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Agents respond to Internal and External
events by selcting an appropriate plan in ts
plan based whose trigger and context is true
When a plan is adopted it becomes an
intention
This intention become part of the agent’s
intention structure
PRS Operation
1. Perceive the world, and update the set of events.
2. For each event, generate the set of plans whose trigger condition
matches the event. These are known as the relevant plans of an
event.
3. For each event, select the subset of relevant plans whose context
condition is satisfied by the agent’s current beliefs. These plans
are known as active plans.
4. From the set of active plans, select one for execution so that it is
now an intention.
5. Include this new intention in the current intention structure
either by creating a new intention stack or by placing it on the
top of an existing stack.
6. Select an intention stack, take the topmost intention, and
execute the next formula in it.
AgentSpeak(L)
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Attempt to bridge the gap between BDI
theories (highly abstract) and implemented
BDI systems
It is an abstraction of an implemented BDI
system
Captures essence of a PRS system
AgentSpeak(L) Data Structures
and Operation
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Beliefs
Intentions (sequences of plans)
Plans have a trigger, context and a sequence
of actions and goals
Agents instantiate plans in response to
internal events (goals of executing plans) and
external events (which effect a belief change)
Agent execute the intentions
Hybrid Agent Architectures
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Solely Reactive Systems
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Solely Deliberative Systems
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Cannot reason about their actions
Will never be able to achieve sophisticated behaviour
Will never be able to act “in time”
If agents are going to be able to survive in complex
dynamic environments and achieve complex goals
they will need to be If agents are going to be able
to survive in complex dynamic environments and
achieve complex goals they will need to be both
reactive and deliberative
Touring Machines
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Based on the following observations:
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Agents need the ability to deal with unexpected events in
the real (physical or electronic) world, and do so at
different levels of granularity.
Agents need to deal with the dynamism in the environment
created by the actions of other agents.
Agents must pay attention to environmental change.
Agents need to reason about temporal constraints in the
knowledge that computation is necessarily resourcebounded.
Agents must reason about the impact the short-term
actions may have on long-term goals.
TouringMachines Architectural
Layers
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Reactive layer
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Planning Layer
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Responds to events not explicitly programmed in
other layers such as obstacle avoidance
Generating, executing and modifying plans
Modelling Layer
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Builds and maintains models of other agents in
the environment
TouringMachine Architecture
TouringMachines
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Each layer models world at different level of
abstraction
Each directly connected to perception and action
Any two layers can communicate
Global rules used when conflicts between layers
arise and can surpress perception or action
Important for showing how to build an agent which
exhibited a range of behaviours from deliberative to
reactive
InterRRaP
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Another layered architecture
Based on BDI
It is vertically layered in constrast to
TouringMachines which are horizontally layered
Horizontally layered – each layer can interact with
every other layer and with perceptions and actions
Vertically layered – communication only possible
between adjacent layers
InterRRaP has four such layers
The essential operation of the agent; in response to
events in the environment, control spreads upwards
until the appropriate level is reached.
InteRRaP Architecture
Distributed Agent Architectures
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Agents described previously may have architecture
that enables them to interact effectively with others
at run time
Here the design is concerned with individual agents
and the dimensions required – micro-level view
Macro-level view – considers a multiagent system
from a holistic view, where interaction, coordination
and cooperation between agents are designed in
advance of run time
Contract Net Protocol Overview
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Most commonly employed coordination mechanism
for controlling the problem solving behavior of a
collection of distributed agents
concerned with the dynamic configuration and
coordination of agents to form hierarchies that can
achieve complex, distributed tasks.
provides a mechanism by which nodes (or agents)
can dynamically create relationships in response to
the current processing requirements of the system
as a whole, thereby enabling opportunistic task
allocation.
Contract Net Protocol
1. A node decomposes a task into a number of subtasks and
issues a task announcement describing what needs to be
performed with eligibility requirements
2. Free nodes bid if they fulfil the eligibility specification
3. Once all bids have been received they are ranked and a
contract is awarded to the highest ranked bidder
4. There is now a contract between the manager who made
the task announcement and the bidder (the contractor).
Over time, the manager monitors the problem solving and
integrates partial results
5. The manager terminates a contract with a termination
message.
Contract Net Protocol
The Contract Net Hierarchy
Subcontracting may take place
Contract Net Summary
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The contract net protocol was a novel means for
dynamically allocating tasks in distributed systems
Still commonly found in many agent systems.
However, it offers a very simple coordination mechanism
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does not provide any strategies for global coherence
only suited to situations in which the tasks are easily
decomposable into
only suitable if agents are not in conflict
does not support mechanisms for bargaining or negotiating
We use it to illustrate general principles involved when
considering the design of systems from the macrolevel
perspective.
Agentis:
Agent Communication Languages
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Agent Communication Languages
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Encodes
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FIPA ACL
KQML
Domain specific content
Set of addressess
Illocutionary
Typically do not encode how a particular message
should be used in the context of a wider
communication episode
Agentis – includes set of agent protocols for reliable
concurrent request and provision of services and
tasks from and to agents.
Agentis
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Agent Interaction Model
Includes the notions of Services and Tasks
Services are requested by human users
Services may call upon a series of tasks as
part of a service contract
Other Approaches to
MacroLevel Organisation
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DECAF
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RETSINA
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Rapid design and execution of agents
Focus on defining generic behaviours that can be re-used
a multiagent infrastructure that comprises a set of services,
conventions, and knowledge that enable complex social
interactions such as negotiation to take place
TeamCore
based on principle that an agent integration infrastructure
(a set of services and conventions) based on theoretical
principles of coordination can automate robust coordination
among heterogeneous agents in distributed systems
Other Agent Approaches:
Agent-Oriented Programming
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Another approach to building agents is to
design a programming language with
semantics based on some theory of rational
or intentional agency, such as the BDI model,
and to program the desired behavior of
individual agents directly using mental
attitudes.
Agent-Oriented Programming
Agent-Oriented Programming
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program comprises a set of transition rules
that specify how an agent in a given mental
state will respond to an input, which may be
a set of messages from other agents, by
defining its new mental state and any
outputs.
One example is PLACA based on AGENT0
which takes a societal view (agents interact
with each other to achieve their own goals)
PLACA Program
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Agent’s state consists of capabilities and its mental state, which
comprises beliefs, intentions, and plans.
At every step, an agent collects messages that have been
received from others and updates its mental state according to
its defining program.
At the beginning of each execution step, those transition rules
that are satisfied in the current state are identified, and applied
to the current mental state and messages collected from the
input buffer.
Once the mental state is updated, messages that need to be
sent are placed in the output buffer, and actions that need to be
performed are recorded and executed in the next step.
If there is sufficient time before the next tick of the clock, the
planner may construct and refine current plans for satisfying
intentions.
PLACA Interpreter
Concurrent MeTaTeM
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Also takes a societal view
Agents specified using a temporal logic that
is directly executable
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Agents specified using temporal logic formula
An interpreter attempts to construct a model in
which the formula is true
Dynamically changing environment also alters the
model so responses must be made to what has
just been made true or untrue in the model
Specification of a Controller
agent in Concurrent MetaTeM
1. If an agent asks, then eventually give the
resource to that agent;
2. Do not give to anyone unless they have
asked since you last gave to them;
3. If you give to two people, then they must be
the same person.
Summary
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Deliberative vs Reactive
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design for decisions at run time - design occurs
before run time
Micro vs Macro
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design for decisions at run time - design occurs
before run time