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7CCSMAMS: Agents & Multi-Agent Systems
Agent architectures
Some slides adapted from slides made available by M. Wooldridge, S. Parsons & T. Payne http://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/imas/IMAS2e
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Response to preliminary module feedback
Reasoning agents
Specifying rules for agent behaviour
Alternative architectures for agents
Today
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Preliminary module feedback summary
…will be presented in the lecture once the survey has been completed by students
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Motivating example: autonomous vehicle convoy
An autonomous vehicle in a convoy needs rules for staying in formation and reaching its destination.
But it also needs autonomy to react to situations.
How can we specify rules of behaviour while retaining autonomy to react?
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General motivating questions for today
What can it mean for an agent to reason about what best to do to meet its goals?
How can reasoning logic be specified while allowing for current circumstances to be accounted for?
If reacting to the environment is the primary concern, then is it better to start from considering how to do this rather than how to reason?
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Deductive reasoning agents
Classical approach to building agents: view them as a particular type of knowledge-based system, bring all the associated methodologies of such systems to bear.
Symbolic AI paradigm.
Contains an explicitly represented, symbolic model of the world.
Makes decisions (for example about what actions to perform) via symbolic reasoning.
Deductive reasoning = particular symbolic approach where representations are logical formulas and reasoning used is logical deduction (theorem proving).
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Deductive reasoning agents
Two key problems:
The representation/reasoning problem:
how to symbolically represent information about complex real-world entities and processes, and how to get agents to reason with this information in time for the results to be useful. Includes: knowledge representation, automated reasoning, automatic planning.
The transduction problem:
how to translate the real world into an accurate, adequate symbolic description, in time for that description to be useful. Includes: computer vision, speech understanding, audio processing.
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Deductive reasoning agents
Most researchers accept that neither problem is anywhere near solved.
Underlying problem lies with the complexity of symbol manipulation algorithms in general: many (most) search-based symbol manipulation algorithms of interest are highly intractable.
(See advantages of reactive agents later.)
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Deductive reasoning agents
How can an agent decide what to do using theorem proving?
Basic idea: use logic to encode a theory stating the best action to perform in any given situation.
Let:
ρ be this theory (typically a set of rules)
Δ be a logical database that describes the current state of the world
Ac be the set of actions the agent can perform
Δ |-ρ ϕ means that ϕ can be deduced from Δ using ρ
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Deductive reasoning agents
ρ = { Day(tuesday) Time(9am) Do(goToAgentsLecture),
Day(friday) Time(7pm) Do(goToDinner) }
Δ = { Day(friday),
Time(7pm) }
Ac = { goToAgentsLecture, goToDinner }
Δ |ρ ??
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Deductive reasoning agents
ρ = { Day(tuesday) Time(9am) Do(goToAgentsLecture),
Day(friday) Time(7pm) Do(goToDinner) }
Δ = { Day(friday),
Time(7pm) }
Ac = { goToAgentsLecture, goToDinner }
Δ |ρ goToDinner
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Deductive reasoning agents
/* try to find an action explicitly prescribed */
for each a ∈ Ac do
if Δ |ρ Do(a) then
return a
end-if
end-for
/* try to find an action not excluded */
for each a ∈ Ac do
if Δ |ρ ¬Do(a) then
return a
end-if
end-for
return null /* no action found */
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Deductive reasoning agents
ρ = {Day(saturday) ¬ Do(goToAgentsLecture)
Day(tuesday) Time(9am) Do(goToAgentsLecture)
Day(friday) Time(7pm) Do(goToDinner) }
Δ = { Day(saturday), Time(7pm) }
Ac = { goToAgentsLecture, goToDinner }
Δ |ρ ??
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Deductive reasoning agents
ρ = {Day(saturday) ¬ Do(goToAgentsLecture)
Day(tuesday) Time(9am) Do(goToAgentsLecture)
Day(friday) Time(7pm) Do(goToDinner) }
Δ = { Day(saturday), Time(7pm) }
Ac = { goToAgentsLecture, goToDinner }
Δ |ρ goToDinner
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Deductive reasoning agents
An example: The Vacuum World.
Goal is for the robot to clear up all dirt.
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Deductive reasoning agents
Use 3 domain predicates to solve problem:
In(x, y) agent is at (x, y)
Dirt(x, y) there is dirt at (x, y)
Facing(d) the agent is facing direction d
Possible actions:
Ac = {turn, forward, suck}
(turn means “turn right”)
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Deductive reasoning agents
Rules ρ for determining what to do:
In(x,y) ∧ Dirt(x,y) → Do(suck)
In(0,0) ∧ Facing(north) ∧ ¬Dirt (0,0) → Do (forward)
In(0,1) ∧ Facing(north) ∧ ¬Dirt (0,1) → Do (forward)
In(0,2) ∧ Facing(north) ∧ ¬Dirt (0,2) → Do (turn)
In(0,2) ∧ Facing(east) → Do (forward)
…and so on!
Using these rules (+ other obvious ones), starting at (0,0) the robot will clear up dirt
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Planning agents
Rather than theorem proving, planning can be a more intuitive way of reasoning to meet goals. Planning agents find a sequence of actions that transforms an initial state into a goal state.
G
a1
a17
a142
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Reasoning via planning: STRIPS example
The stack action occurs when the robot arm places the object x it is holding is placed on top of object y.
Stack(x, y)
pre Clear(y) ∧ Holding(x)
del Clear(y) ∧ Holding(x)
add ArmEmpty ∧ On(x, y)
Similarly, there are unstack, pickup, putdown… actions
A
B
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Problems with deductive reasoning agents
Problems:
How to convert video camera input to Dirt(0, 1) or On(B, Table)?
Decision making using first-order logic is undecidable
Even where we use propositional logic, decision making in the worst case means solving co-NP-complete problems
Decision making assumes a static environment
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Calculative rationality
“An agent is said to enjoy the property of calculative rationality if and only if its decision-making apparatus will suggest an action that was optimal when the decision-making process began” – time t1, the action function of the agent takes as input its internal state at that point Δ1 and uses its rule set ρ to determine what the best action to perform is. After some time, at t2, it manages to establish Δ1 |ρ for some Ac.
If the agent has the property of calculative rationality, then is an action that is guaranteed to be optimal at time t1.
What if the world has changed between t1 and t2?
In that case is no longer guaranteed to be optimal.
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Accommodating dynamic environments
A reasoning agent in a dynamic environment, or in a multi-agent system in any environment, needs to meet two demands
It needs to be able to apply rules to determine what to do
It needs to not be constrained by those rules in having to do something now, when it may have to react to other events
We need rules to talk about acting ‘sometime in the future’ and similar, e.g. using temporal logic
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Temporal logic
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Temporal logic
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Temporal logic
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Temporal logic
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Temporal logic
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Temporal logic
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Temporal logic
¬ year (2020)
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Temporal logic
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Temporal logic
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Temporal logic
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Temporal logic
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Exercise
We can nest temporal operators and use them with the standard logical operators (negation: ¬, conjunction: ∧, disjunction: ∨, implication: →). What do the following formulas say?
☐(q → ☐ ¬p)
○(◎p))
((lightOn → ○ ¬lightOn) ∧ (¬lightOn → ○ lightOn)) S lightOn
Write the following statement as a temporal logic formula, using the predicates study(agents, you) and knowGreat(agents, you).
Unless you study agents, you won’t know how great they are.
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Concurrent MetateM
Given a temporal logic, we now need a way to express behavioural rules.
An example framework is Concurrent MetateM.
In this framework, an agent’s specification is executed directly to generate behaviour.
Concurrently executing agents communicate via asynchronous broadcast message passing.
Agents are defined by:
the set of message types that they listen for,
the set of message types that they broadcast,
a set of temporal logic formulas that determine their behaviour.
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Concurrent MetateM
Execution proceeds by a process of continually matching rules against a “history”, and firing those rules whose antecedents are satisfied.
The instantiated future-time consequents become commitments which must subsequently be satisfied.
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Concurrent MetateM
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Concurrent MetateM
Agent identifiers
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Concurrent MetateM
Message types each agent listens for
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Concurrent MetateM
Message types each agent can broadcast
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Concurrent MetateM
Temporal logic formulas that determine agent behaviour
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Concurrent MetateM
Agent execution:
at each time point:
1. Set environment propositions according to messages received;
2. Check which rules fire by comparing antecedents with history;
3. Jointly execute fired rule consequents together with commitments carried over from previous cycles.
When trying to satisfy commitments, agent gives preference to its oldest commitments.
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Concurrent MetateM
What is the behaviour of this system?
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Concurrent MetateM
A possible run of system
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Concurrent MetateM
A possible run of system
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Concurrent MetateM
A possible run of system
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Concurrent MetateM
A possible run of system
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Concurrent MetateM
A possible run of system
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Concurrent MetateM
A possible run of system
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Concurrent MetateM
A possible run of system
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Concurrent MetateM
A possible run of system
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Concurrent MetateM
A possible run of system
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Concurrent MetateM
A possible run of system
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Concurrent MetateM
Satisfaction of commitments
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Exercise
Trace the first 6 time steps of the system below.
snowWhite (ask(X)) [give(X)]: ◎ ask(X) → ◇give(X)
start → □ ¬(give(X) ⌃ give(Y) ⌃ X ≠ Y)
eager (give(eager)) [ask(eager)]: start → ask(eager)
◎ give(eager → ask(eager)
greedy () [ask(eager)]: start → □ ask(greedy)
courteous (give(eager), give(greedy)) [ask(courteous)]:
((¬ ask(courteous) S give(eager)) ⌃ (¬ ask(courteous) S give(greedy))) →
ask (courteous)
shy (give(shy), ask(X)) [ask(shy)]: start → ◇ask(shy)
◎ ask(X) → ¬ ask(shy)
◎ give(shy) → ◇ask(shy)
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Reasoning versus reactivity
Temporal logic allows us to separate what happens now from what happens sometime, allowing some reactivity.
But the reasoning is still resource demanding, and the connection between the real environment and the symbols representing it is problematic.
We could start our agent design from their reactivity rather than their reasoning.
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Brooks: Subsumption architecture
Brooks put forward three theses:
Intelligent behaviour can be generated without explicit representations of the kind that symbolic AI proposes.
Intelligent behaviour can be generated without explicit abstract reasoning of the kind that symbolic AI proposes.
Intelligence is an emergent property of certain complex systems.
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Brooks: Subsumption architecture
He identifies two key ideas that have informed his research:
Situatedness and embodiment: ‘Real’ intelligence is situated/embedded in the world, not in disembodied systems such as theorem provers or expert systems.
Intelligence and emergence: ‘Intelligent’ behaviour arises as a result of an agent’s interaction with its environment. Also, intelligence is ‘in the eye of the beholder’; it is not an innate, isolated property.
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Brooks: Subsumption architecture
A subsumption architecture is made up of task-accomplishing behaviour modules.
No (or not much) processing of data.
No complex symbolic representations.
No symbolic reasoning.
if SITUATION then ACTION
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Brooks: Subsumption architecture
Many behaviours might fire at once. Which one to perform?
Behaviours organized into a hierarchy. Lower layers take precedence over higher layers.
In terms of computation, extremely simple. But still can perform impressive tasks!
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Brooks: Subsumption architecture
Flikr: Penn State
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Brooks: Subsumption architecture
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Example subsumption architecture application
Steels’ Mars explorer system, using the subsumption architecture, achieves near-optimal cooperative performance in simulated ‘rock gathering on Mars’ domain.
The objective is to explore a distant planet, and in particular, to collect samples of a precious rock. The location of the samples is not known in advance, but it is known that they tend to be clustered
Mother ship emits a radio wave.
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Example subsumption architecture application
For individual (non-cooperative) agents, the lowest-level behaviour, (and hence the behaviour with the highest “priority”) is obstacle avoidance:
If detect an obstacle then change direction
If carrying samples and at the base then drop samples
If carrying samples and not at the base then travel up radio wave gradient
If detect a sample then pick sample up
If true then move randomly
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Example subsumption architecture application
if detect an obstacle then change direction
if carrying samples and at
the base then drop samples
if carrying samples and not at the base
then travel up radio wave gradient
if detect a sample then pick sample up
if true then move randomly
percept
action
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Example subsumption architecture application
if detect an obstacle then change direction
if carrying samples and at
the base then drop samples
if carrying samples and not at the base
then travel up radio wave gradient
if detect a sample then pick sample up
if true then move randomly
percept
action
Environment
Agent
see
action
see : E → Per
action : Per → Ac
Slide ‹#› out of 95
Some slides adapted from slides made available by M. Wooldridge, S. Parsons & T. Payne http://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/imas/IMAS2e
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Example subsumption architecture application
For cooperative agents, the lowest-level behaviour, is obstacle avoidance:
If detect an obstacle then change direction
If carrying samples and at the base then drop samples
If carrying samples and not at the base then travel up radio wave gradient and drop 2 crumbs
If detect a sample then pick sample up
If sense crumbs then pick up 1 crumb and travel down radio wave gradient
If true then move randomly
Slide ‹#› out of 95
Some slides adapted from slides made available by M. Wooldridge, S. Parsons & T. Payne http://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/imas/IMAS2e
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Example subsumption architecture application
if detect an obstacle then change direction
if carrying samples and at
the base then drop samples
if carrying samples and not at the base then travel up radio wave gradient and drop 2 crumbs
if detect a sample then pick sample up
if true then move randomly
percept
action
if sense crumbs then pick up 1 crumb
and travel down radio wave gradient
Slide ‹#› out of 95
Some slides adapted from slides made available by M. Wooldridge, S. Parsons & T. Payne http://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/imas/IMAS2e
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Example subsumption architecture application
Steels shows that cooperative agents achieve near-optimal performance in many situations.
Solution is cheap and robust.
Slide ‹#› out of 95
Some slides adapted from slides made available by M. Wooldridge, S. Parsons & T. Payne http://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/imas/IMAS2e
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Discussion
What are the limitations of reactive agents, such as those using the subsumption architecture?
In what cases would it be hard to engineer practical agent(s) using this approach?
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Some slides adapted from slides made available by M. Wooldridge, S. Parsons & T. Payne http://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/imas/IMAS2e
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Hybrid architectures
Many researchers have argued that neither a completely deliberative nor completely reactive approach is suitable for building agents.
They have suggested using hybrid systems, which attempt to marry classical and alternative approaches.
An obvious approach is to build an agent out of two (or more) subsystems:
a deliberative one, containing a symbolic world model, which develops plans and makes decisions in the way proposed by symbolic AI;
a reactive one, which is capable of reacting to events without complex reasoning.
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Hybrid architectures
Often, the reactive component is given some kind of precedence over the deliberative one.
This kind of structuring leads naturally to the idea of a layered architecture, of which TOURINGMACHINES and INTERRAP are examples.
In such an architecture, an agent’s control subsystems are arranged into a hierarchy, with higher layers dealing with information at increasing levels of abstraction.
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Hybrid architectures
A key problem in such architectures is what kind of control framework to embed the agent’s subsystems in, to manage the interactions between the various layers.
Horizontal layering
Layers are each directly connected to the sensory input and action output.
In effect, each layer itself acts like an agent, producing suggestions as to what action to perform.
Vertical layering
Sensory input and action output are each dealt with by at most one layer each.
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Some slides adapted from slides made available by M. Wooldridge, S. Parsons & T. Payne http://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/imas/IMAS2e
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Hybrid architectures
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m possible actions suggested by each layer, n layers
Introduces bottleneck
in central control system
Not fault tolerant to layer failure
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Next week we move on to multi-agent systems, starting with agent-based modelling (simulation) before focusing on methods for achieving coordination, competition and agreement between agents.
Next week
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