Artificial Intelligence - Problems, State Space Search & Heuristic Search Techniques - Defining the Problems as a State Space Search Production Systems Production Characteristics Production System Characteristics Issues in the design of Search Programs

1. PANKAJ DEBBARMA
Deptt. of CSE, TIT, Narsingarh

2. Problems, State Space Search &
Heuristic Search Techniques
• Defining the Problems as a State Space Search
• Production Systems
• Production Characteristics
• Production System Characteristics
• Issues in the design of Search Programs

3. task environments
• Fully observable vs. partially observable
• Single agent vs. multiagent
• Competitive vs. cooperative
• Deterministic vs. stochastic
• Episodic vs. sequential
• Static vs. dynamic
• Discrete vs. continuous
• Known vs. unknown

4. • The environment is all the information about
the world that remains constant while we are
solving the problem.
• A state is a set of properties that define the
current conditions of the world our agent is in.
– Think of this as a snapshot of the world at a given
point in time.
– The entire set of possible states is called the state
space.
• The initial state is the state the agent begins
in.
• A goal state is a state where the agent may
end the search.

5. problems, state space, search
• An agent may take different actions that will
lead the agent to new states.
• Intelligent agents are supposed to maximize
their performance measure.
• One general formulation of intelligent action is
in terms of state space.
• A state contains all of the information
necessary to predict the effects of an action
and to determine if it is a goal state.

6. problems, state space, search
• The agent has perfect knowledge of the state
space and can observe what state it is in;
• The agent has a set of actions that have known
deterministic effects;
• Some states are goal states, the agent wants to
reach one of these goal states, and the agent can
recognize a goal state;
• A solution is a sequence of actions that will get
the agent from its current state to a goal state.

7. problems, state space, search
• Intelligent agents are supposed to maximize
their performance measure.
• Goal formulation, based on the current
situation and the agent’s performance
measure, is the first step in problem solving.
• Problem formulation is the process of
deciding what actions and states to consider,
given a goal.

8. problems, state space, search
• The process of looking for a sequence of
actions that reaches the goal is called search.
• A search algorithm takes a problem as input
and returns a solution in the form of an
action sequence.
• Once a solution is found, the actions it
recommends can be carried out. This is called
the execution phase.

11. Karisimbi
KIGALI
Gitarama
Butare
Cyangugu
Gisenyi
Ruhengeri
Byumba
Nyarugumba
Kisiguru
Kibungo
Gikongoro
Ngoma
Murunda
Kibuye

12. problems, state space, search
A problem can be defined formally by five
components:
1. The initial state that the agent starts in.
2. A description of the possible actions available to
the agent.
3. A description of what each action does; the
formal name for this is the transition model.
– We also use the term successor to refer to any state
reachable from a given state by a single action.

13. problems, state space, search
– Together, the initial state, actions, and transition
model implicitly define the state space of the
problem—the set of all states reachable from the
initial state by any sequence of actions.
– The state space forms a directed network or graph
in which the nodes are states and the links
between nodes are actions.
– A path in the state space is a sequence of states
connected by a sequence of actions.
4. The goal test, which determines whether a
given state is a goal state.

14. problems, state space, search
5. A path cost function that assigns a numeric cost
to each path.
• The preceding elements define a problem and
can be gathered into a single data structure that
is given as input to a problem-solving algorithm.
• A solution to a problem is an action sequence
that leads from the initial state to a goal state.
• Solution quality is measured by the path cost
function, and an optimal solution has the lowest
path cost among all solutions.

15. problems, state space, search
• The process of removing detail from a
representation is called abstraction.
• The abstraction is valid if we can expand any
abstract solution into a solution in the more
detailed world.
• The abstraction is useful if carrying out each
of the actions in the solution is easier than the
original problem

16. problems, state space, search
Any problem can be solved by the following series
of step:
1. Define a state space which contain all the
possible configuration of the relevant object.
2. Specify one or more states within that space
which would describe possible situation from
which find out the initial states.
3. Specify one or more states which would be
acceptable as solution to the problem .the state
are called goal states.
4. Specify a set of rules which describe the actions
(operators) available and a control strategy to
decide the order of application of these rules.

17. problems, state space, search
The most common methods of problem solving
representation in AI.
1. State space representation
2. Problem reduction

18. state space representation
State space
representation for
coffee making

19. production system
• The main function of the production system is
to provide a useful tool for problem solving in
AI Production System are Frequently referred
to as inferential system or rule base –based
system or production system.
• It is useful to structure AI problems in a way
that facilitates describing & performing the
search process. Production systems provide
these structures.

20. production system
A production system consists of following components
• A set of rules each consisting of a left side that
determines the acceptability of the rule and a right
that describes the operation to be performed if the
rule is applied.
• One or more knowledge bases that contain whatever
information are appropriate for a particular task.
• A control strategy that specifies the order in which in
which the rules will be compared to the database and a
way of resolving the conflicts that arise when several
rules match at once.
• 4. A rule applier(user).

21. production system
Block diagram
of production
system
Control Strategy

22. advantages of production systems
1. Production systems provide an excellent tool
for structuring AI programs.
2. Production Systems are highly modular
because the individual rules can be added,
removed or modified independently.
3. The production rules are expressed in a
natural form, so the statements contained in
the knowledge base should the a recording
of an expert thinking out loud.

23. disadvantages of production systems
• One important disadvantage is the fact that it
may be very difficult analyze the flow of
control within a production system because
the individual rules don’t call each other.