Machine Intelligence Cairo University Faculty of Engineering
Computer Engineering Department Course Instructor: Prof. Dr. Nevin
Darwish
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Team members Sylvia Boshra Lydia Wahid Madonna Samuel Wessam
Wagdy
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Artificial Intelligence A Modern Approach 3rd Edition Chapter
11
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Planning and Acting in the Real World
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Agenda 1. Recall classical planning 2. Types of the environment
3. Methods To deal with different types of the environment
I.Sensorless (Conformant)Planning II.Contingent Planning III.Online
Replanning 4. Multiagent Planning I.Planning with multiple
simultaneous actions II.Planning with multiple agents 5.
Summary
3. Methods To deal with different types of the environment I.
Sensorless (Conformant)Planning II. Contingent Planning III. Online
Replanning
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I. Sensorless Planning Belief state Belief state for the
coloring problem Object(Table) Object(Chair) Can(C1) Can(C2)
Color(x,C(x)) b o = Color(x,C(x)) Open-world assumption
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Using belief state to reach the goal Update belief state
b=RESULT(b,a)=(b-DEL(a)) ADD(a)
Using belief state to reach the goal Update belief state
b=RESULT(b,a)=(b-DEL(a)) ADD(a) After applying action
RemoveLid(Can1) b 1 = Color(x,C(x)) Open(Can1)
Using belief state to reach the goal Update belief state
b=RESULT(b,a)=(b-DEL(a)) ADD(a) After applying action
RemoveLid(Can1) b1 = Color(x,C(x)) Open(Can1) After applying action
Paint(Chair, Can1) b2 = Color(x,C(x)) Open(Can1)
Color(Chair,C(Can1)) After applying Paint(Table, Can1) b3=
Color(x,C(x)) Open(Can1) Color(Chair,C(Can1))
Color(Table,C(Can1))
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Goal(Color(Chair,c) Color(Table,c) ) Belief state satisfies the
goal.
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Problem Vacuum world Belief state= AtL AtR If we applied action
Suck There is a problem: Two different effects!! If AtL, effect:
CleanL If AtR, effect:CleanR
3. Conservative approach Look for action sequences that keep
the belief state as simple as possible Retain 1-CNF belief state
Some sequences can go outside 1-CNF
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Agenda 1. Recall classical planning 2. Types of the environment
3. Methods To deal with different types of the environment
I.Sensorless (Conformant)Planning II.Contingent Planning III.Online
Replanning 4. Multiagent Planning I.Planning with multiple
simultaneous actions II.Planning with multiple agents 5.
Summary
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II. Contingent Planning: Whats contingent planning: Whats
contingent planning: Contingent planning is the generation of plans
with conditional branching based on percepts. It is appropriate for
environments with partial observability and/or non-
determinism.
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After an action and subsequent percept, calculating the new
belief state is done in two stages: 1.Calculating the belief state
after the action. 2.Updating the belief state after perception of
the environment. If a percept P has more than one percept axiom
then we have to add the disjunction (OR) of the preconditions.
Which will take the belief state out of CNF (and of ors). We can
generate contingent plans with an extension of the AND-OR forward
search over belief states.
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III. Online Planning Example: The spot-welding agent in a car
plant: The robot welds the same accurate position in every car that
passes down the line, if a car door falls off a car as the robot is
trying to apply a spot weld, the robot replaces the welding
actuator with a gripper, picks up the door, checks for scratches,
reattaches it to the car, email the floor supervisor, switches back
to its welding actuator and continues its work. The robots behavior
seems purposive. The robot knows what its trying to do.
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Conditions for Replanning: Execution monitoring to determine
the need for a new plan. The need for a new plan arises when a
contingent planning agent gets tired of planning for every
contingency such as the sky might fall on its head.
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Needs for Replanning: 1.If the agents model of the world is
incorrect. 2.If the agents model of an action have a missing
precondition. Example: Opening a can of paints involves using a
screwdriver to remove the lid. 3.If the agents model have a missing
effect. Example: Painting a chair may get paint on the floor. 4.If
the agents model is missing a state variable. Example: How the
amount of paint in the can can effect the agents actions. 5.If the
agents model is lacking provision for exogenous events. Events that
are out of the hands of the agent, like someone knocking over the
can of paint.
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Without monitoring and replanning, the agents behavior is as
fragile if it relies on absolute correctness of its model. Levels
of monitoring the environment: Action monitoring: Before the
execution of an action, the agent verifies that all the
preconditions still hold. Plan Monitoring: Before the execution of
an action, the agent verifies that the remaining plan will still
succeed. Goal Monitoring: Before the execution of an action, the
agent checks to see if there is a better set of goals it could be
trying to achieve.
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Action monitoring Vs. Plan Monitoring: Action monitoring is a
simple method of execution monitoring, but it can sometimes lead to
less than intelligent behavior. Example: The agent constructs a
plan to solve the painting problem by painting both the chair and
the table red. Suppose that there is only enough red paint for the
chair. With action monitoring, the agent would go ahead and paint
the chair red, then notice that it is out of paint and cannot paint
the table, at which it would replan a repair, painting the chair
and table green. A plan monitoring agent can detect failure
whenever the current state is such that the plan no longer works.
Thus, it would not waste time painting a chair red.
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Does it work? We cannot guarantee that the agent always reaches
the goal, Because it could arrive to a dead end state from which
there is no repair. For example, the Vacuum cleaner agent may have
a faulty model of itself and doesnt know that its batteries may run
out. For the agent to always reach the goal, we must assume that:
1. There are no dead ends, the goal is reachable from all states in
the environment. 2.Environment is really non-deterministic.
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When actions are not really non- deterministic: Trouble occurs
when actions depend on some precondition the agent doesnt know
about. Example: The painting agent may not know that the paint can
is empty and no amount of retrying to paint would reach the goal.
Two Approaches to solve this problem: Choosing randomly among the
set of possible repair plans, rather than trying the same repair.
Learning: The agent should be able to modify its model of the world
to accord with its percepts.
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Agenda 1. Recall classical planning 2. Types of the environment
3. Methods To deal with different types of the environment
I.Sensorless (Conformant)Planning II.Contingent Planning III.Online
Replanning 4. Multiagent Planning I.Planning with multiple
simultaneous actions II.Planning with multiple agents 5.
Summary
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4. MULTIAGENT PLANNING When there are multiple agents in the
environment, each agent faces a multiagent planning problem.
Between the purely single-agent and truly multiagent cases is a
wide spectrum of problems. Examples: human who can type and speak
at the same time A fleet of delivery robots in a factory 34
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The multiple bodies act as a single body as long as the
relevant sensor information collected by each body can be pooled
form a common estimate of the world state that then informs the
execution of the overall plan. When communication constraints make
this impossible, we have a decentralized planning problem Example:
Multiple reconnaissance robots covering a wide area 35
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When a single entity is doing the planning, there is really
only one goal, which all the bodies necessarily share. When the
bodies are distinct agents that do their own planning, they may
still share identical goals Example: two human tennis players who
form a doubles team share the goal of winning the match. The
multibody and multiagent cases are quite different In a multibody
robotic doubles team 36
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The clearest case of a multiagent problem, of course is when
the agents have different goals. Example: In tennis, the goals of
two opposing teams are in direct conflict. Some systems are a
mixture of centralized and multiagent planning. Example: a delivery
company 37
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The issues involved in multiagent planning can be divided
roughly into two sets: 1. involves issues of representing and
planning for multiple simultaneous actions; these issues occur in
all settings from multieffector to multiagent planning. 2. involves
issues of cooperation, coordination, and competition arising in
true multiagent settings. 38
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I. Planning with multiple simultaneous actions For the time
being, we will treat the multieffector, multibody, and multiagent
settings in the same way. A correct plan is one that, if executed
by the actors, achieves the goal. We assume perfect
synchronization: each action takes the same amount of time and
actions at each point in the joint plan are simultaneous. 39
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40
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If the actors have no interaction with one then we can simply
solve n separate problems.(Example: n actors each playing a game of
solitaire). The standard approach to loosely coupled problems is to
pretend the problems are completely decoupled and then fix up the
interactions. For the transition model, this means writing action
schemas as if the actors acted independently. 41
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Problems arise, however, when a plan has both agents hitting
the ball at the same time. Technically, the difficulty is that
preconditions constrain the state in which an action can be
executed successfully, but do not constrain other actions that
might mess it up. 42
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A concurrent action list stating which actions must or must not
be executed concurrently. the Hit action has its stated effect only
if no other Hit action by another agent Occurs at the same time.
43
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For some actions, the desired effect is achieved only when
another action occurs concurrently. Example: two agents are needed
to carry a cooler full of beverages to the tennis court 44
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Agenda 1. Recall classical planning 2. Types of the environment
3. Methods To deal with different types of the environment
I.Sensorless (Conformant)Planning II.Contingent Planning III.Online
Replanning 4. Multiagent Planning I.Planning with multiple
simultaneous actions II.Planning with multiple agents 5.
Summary
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II. Planning with multiple agents: Co-operation and
Co-ordination 46 Actors(A,B) Init(At(A, LeftBaseline)
At(B,RightNet) Approaching(Ball,RightBaseline)) Partner(A,B)
Partner(B,A) Goal(Returned(Ball) (At(a,RightNet) OR At(a,LeftNet))
Action(Hit(actor,Ball), PRECOND: Approaching(Ball,loc)
AT(actor,loc) EFFECR: Returned(Ball)) Action(Go(actor,to), PRECOND:
AT(actor,loc) to loc, EFFECT: At(actor,to) At(actor,loc)) Plan1 A:
[Go(A, RightBaseline), Hit( A, Ball)] B: [NoOp(B), NoOp(B)] Plan2
A: [Go(A, LeftNet), NoOp(A)] B: [Go(B, RightBaseline), Hit( B,
Ball)]
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II. Planning with multiple agents: Co-operation and
Co-ordination 47 Initially Init(At(A, LeftBaseline) At(B,RightNet)
Approaching(Ball,RightBaseline)) Partner(A,B) Partner(B,A)
Goal(Returned(Ball) (At(a,RightNet) OR At(a,LeftNet))
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II. Planning with multiple agents: Co-operation and
Co-ordination 48 Plan 1 A: [Go(A, RightBaseline), Hit( A, Ball)] B:
[NoOp(B), NoOp(B)]
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II. Planning with multiple agents: Co-operation and
Co-ordination 49 Plan 1 A: [Go(A, RightBaseline), Hit( A, Ball)] B:
[NoOp(B), NoOp(B)]
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II. Planning with multiple agents: Co-operation and
Co-ordination 50 Plan 1 A: [Go(A, RightBaseline), Hit( A, Ball)] B:
[NoOp(B), NoOp(B)] The GOAL
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II. Planning with multiple agents: Co-operation and
Co-ordination 51 Plan 2 A: [Go(A, LeftNet), NoOp(A)] B: [Go(B,
RightBaseline), Hit( B, Ball)]
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II. Planning with multiple agents: Co-operation and
Co-ordination 52 Plan 2 A: [Go(A, LeftNet), NoOp(A)] B: [Go(B,
RightBaseline), Hit( B, Ball)]
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II. Planning with multiple agents: Co-operation and
Co-ordination 53 Plan 2 A: [Go(A, LeftNet), NoOp(A)] B: [Go(B,
RightBaseline), Hit( B, Ball)]
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II. Planning with multiple agents: Co-operation and
Co-ordination 54 Plan 2 A: [Go(A, LeftNet), NoOp(A)] B: [Go(B,
RightBaseline), Hit( B, Ball)] The GOAL
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II. Planning with multiple agents: Co-operation and
Co-ordination 55 Again initially Init(At(A, LeftBaseline)
At(B,RightNet) Approaching(Ball,RightBaseline)) Partner(A,B)
Partner(B,A) Goal(Returned(Ball) (At(a,RightNet) OR
At(a,LeftNet))
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II. Planning with multiple agents: Co-operation and
Co-ordination 56 Plan 1 by A Plan1 A: [Go(A, RightBaseline), Hit(
A, Ball)] B: [NoOp(B), NoOp(B)]
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II. Planning with multiple agents: Co-operation and
Co-ordination 57 Plan 2 by B Plan2 A: [Go(A, LeftNet), NoOp(A)] B:
[Go(B, RightBaseline), Hit( B, Ball)]
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II. Planning with multiple agents: Co-operation and
Co-ordination 58 Plan 2 by A Plan2 A: [Go(A, LeftNet), NoOp(A)] B:
[Go(B, RightBaseline), Hit( B, Ball)]
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II. Planning with multiple agents: Co-operation and
Co-ordination 59 Plan 2 by A Plan2 A: [Go(A, LeftNet), NoOp(A)] B:
[Go(B, RightBaseline), Hit( B, Ball)]
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II. Planning with multiple agents: Co-operation and
Co-ordination 60 Plan 1 by B Plan1 A: [Go(A, RightBaseline), Hit(
A, Ball)] B: [NoOp(B), NoOp(B)]
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II. Planning with multiple agents: Co-operation and
Co-ordination How to coordinate to make sure they agree on the
plan? 1. Adopt a convention 2. Use communication 1.Verbal Exchange
2.Plan Recognition 61
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II. Planning with multiple agents: Co-operation and
Co-ordination 1. Adopt a convention: Any constraint on selection of
joint plans. Convention: Stick to your side of the court (Plan 1 is
out, both agents will select Plan 2) Any alternative conventions
works equally as long as all agents in an environment agree When
the conventions are widespread then called social laws 62
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II. Planning with multiple agents: Co-operation and
Co-ordination 2. Use communication: To achieve common knowledge of
a feasible joint plan Chapter 22: more mechanisms for commination
Can work as well with competitive agents as with cooperative ones
63
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II. Planning with multiple agents: Co-operation and
Co-ordination 64 Yours ! 2. Use communication 1. By Verbal
Exchange
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II. Planning with multiple agents: Co-operation and
Co-ordination 65 Mine ! 2. Use communication 1. By Verbal
Exchange
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II. Planning with multiple agents: Co-operation and
Co-ordination 66 Then its Plan 1 Plan1 A: [Go(A, RightBaseline),
Hit( A, Ball)] B: [NoOp(B), NoOp(B)]
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II. Planning with multiple agents: Co-operation and
Co-ordination 67 2. Use communication 2. Plan Recognition By
Executing the first part of the plan
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II. Planning with multiple agents: Co-operation and
Co-ordination 68 Its plan 2 2. Use communication 2. Plan
Recognition By Executing the first part of the plan
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II. Planning with multiple agents: Co-operation and
Co-ordination 69 2. Use communication 2. Plan Recognition By
Executing the first part of the plan
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II. Planning with multiple agents: Co-operation and
Co-ordination Another Examples: 1. Seed-Eating harvester ants The
queens job is to reproduce not to do centralized planning There is
some learning mechanism that make them able to make successful
actions over decades-long life even though individual ants live
only about a year 2. Flocking behavior of birds Observes the
position of its nearest neighbors and choose the heading and
acceleration that max the weighted sum of: 1.Cohesion 2.Separation
3.Alignment 70
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5.Summary: Resources as numeric measures Time is handled by
specialized scheduling or integrated with planning HTN planning
using HLAs Effects of HLAs can be defined with angelic semantic
Contingent plans allows the agent to sense the world, Sensor-less
and Contingent plan can be constructed by search in the space of
Belief States Online planning agent can re-plan due to
nondeterministic actions or incorrect models of environment
Multiagents to cooperate or compete and must agree on which joint
plans to be executed This chapter extends classical planning to
cover nondeterministic environments 71
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