Applied Artificial Intelligence Unit 4 Semester 3 MSc IT Part 2 Mumbai University

APPLIED ARTIFICIAL INTELLIGENCE
UNIT - 4

• EVOLUTIONARY COMPUTATION: SOFT COMPUTING, GENETIC
ALGORITHMS, GENETIC PROGRAMMING CONCEPTS, EVOLUTIONARY
PROGRAMMING, SWARM INTELLIGENCE, ANT COLONY PARADIGM,
PARTICLE SWARM OPTIMIZATION AND APPLICATIONS OF
EVOLUTIONARY ALGORITHMS.
• INTELLIGENT AGENTS: AGENTS VS SOFTWARE PROGRAMS,
CLASSIFICATION OF AGENTS, WORKING OF AN AGENT, SINGLE AGENT
AND MULTIAGENT SYSTEMS, PERFORMANCE EVALUATION,
ARCHITECTURE, AGENT COMMUNICATION LANGUAGE, APPLICATIONS
TOPICS TO COVER…!
PPT BY: MADHAV
MISHRA
2

SOFT COMPUTING
• Soft computing is the use of approximate calculations to provide imprecise but
usable solutions to complex computational problems.
• The approach enables solutions for problems that may be either unsolvable or
just too time-consuming to solve with current hardware.
• Soft computing is sometimes referred to as computational intelligence.
• Soft computing provides an approach to problem-solving using means other
than computers.
• With the human mind as a role model, soft computing is tolerant of partial
truths, uncertainty, imprecision and approximation, unlike traditional
computing models.
• The tolerance of soft computing allows researchers to approach some problems
that traditional computing can't process.
PPT BY: MADHAV MISHRA 3

Soft computing uses component fields of study in:
• Fuzzy logic
• Machine learning
• Probabilistic reasoning
• Evolutionary computation
• Perceptron
• Genetic algorithms
• Differential algorithms
• Support vector machines
• Swarm intelligence

• As a field of mathematical and computer study, soft computing has been
around since the 1990s.
• The inspiration was the human mind's ability to form real-world solutions to
problems through approximation.
• Soft computing contrasts with possibility, an approach that is used when there
is not enough information available to solve a problem.
• Soft computing is used where the problem is not adequately specified for the
use of conventional math and computer techniques.
• Soft computing has numerous real-world applications in domestic, commercial
and industrial situations.

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PPT BY: MADHAV MISHRA
• Genetic algorithm (GA) is a search-based optimization technique based
on the principles of genetics and natural selection. It is frequently used to
find optimal or near-optimal solutions to difficult problems which
otherwise would take a lifetime to solve. It is frequently used to solve
optimization problems, in research, and in machine learning.
• Optimization refers to finding the values of inputs in such a way that we
get the “best” output values. The definition of “best” varies from
problem to problem, but in mathematical terms, it refers to maximizing
or minimizing one or more objective functions, by varying the input
parameters.
• The set of all possible solutions or values which the inputs can take
make up the search space. In this search space, lies a point or a set of
points which gives the optimal solution. The aim of optimization is to
find that point or set of points in the search space.
GENETIC ALGORITHMS

• Genetic algorithms (gas) are search based algorithms based on the concepts of
natural selection and genetics. Gas are a subset of a much larger branch of
computation known as evolutionary computation.
• Gas were developed by John Holland and his students and colleagues at the
University of Michigan, most notably David E. Goldberg and has since been tried
on various optimization problems with a high degree of success.
Advantages of gas
• Gas have various advantages which have made them immensely popular. These include −
• Does not require any derivative information (which may not be available for many real-world
problems).
• Is faster and more efficient as compared to the traditional methods.
• Has very good parallel capabilities.
• Provides a list of “good” solutions and not just a single solution.

Limitations of gas
• Like any technique, gas also suffer from a few limitations. These include −
• Gas are not suited for all problems, especially problems which are simple and for which derivative
information is available.
• Fitness value is calculated repeatedly which might be computationally expensive for some
problems.
• Being stochastic, there are no guarantees on the optimality or the quality of the solution.
• If not implemented properly, the GA may not converge to the optimal solution.
BASIC TERMINOLOGY
• Population − it is a subset of all the possible (encoded) solutions to the given
problem..
• Chromosomes − a chromosome is one such solution to the given problem.
• Gene − a gene is one element position of a chromosome.
• Allele − it is the value a gene takes for a particular chromosome.

• Genotype − genotype is the population in the computation space. In the computation space, the
solutions are represented in a way which can be easily understood and manipulated using a
computing system.
• Phenotype − phenotype is the population in the actual real world solution space in which solutions
are represented in a way they are represented in real world situations.
• Decoding and encoding − for simple problems, the phenotype and genotype spaces are the same.
• However, in most of the cases, the phenotype and genotype spaces are different.
• Decoding is a process of transforming a solution from the genotype to the phenotype space,
• While encoding is a process of transforming from the phenotype to genotype space. Decoding
should be fast as it is carried out repeatedly in a GA during the fitness value calculation.
• Fitness function − a fitness function simply defined is a function which takes the solution as input
and produces the suitability of the solution as the output. In some cases, the fitness function and the
objective function may be the same, while in others it might be different based on the problem.
• Genetic operators − these alter the genetic composition of the offspring. These include crossover,
mutation, selection, etc.

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The basic structure of a GA is as follows −
• We start with an initial population
(which may be generated at random or
seeded by other heuristics), select
parents from this population for mating.
Apply crossover and mutation operators
on the parents to generate new off-
springs. And finally these off-springs
replace the existing individuals in the
population and the process repeats.
• In this way genetic algorithms actually
try to mimic the human evolution to
some extent.
BASIC STRUCTURE OF GA

GENETIC PROGRAMMING CONCEPTS
• Genetic programming is a domain-independent method that genetically breeds a population of
computer programs to solve a problem.
• Specifically, genetic programming iteratively transforms a population of computer programs into
a new generation of programs by applying analogs of naturally occurring genetic operations.
Preparatory steps of genetic programming:
• The human user communicates the high-level statement of the problem to the genetic
programming system by performing certain well-defined preparatory steps.
• The five major preparatory steps for the basic version of genetic programming require:
1. The set of terminals (E.G., The independent variables of the problem, zero-argument
functions, and random constants) for each branch of the to-be-evolved program,
2. The set of primitive functions for each branch of the to-be-evolved program,
3. The fitness measure (for explicitly or implicitly measuring the fitness of individuals in the
population),
4. Certain parameters for controlling the run, and
5. The termination criterion and method for designating the result of the run.
PPT BY: MADHAV
MISHRA
13

SWARM INTELLIGENCE
What is a swarm?
• A loosely structured collection of interacting agents
• Agents:
• Individuals that belong to a group (but are not necessarily identical)
• They contribute to and benefit from the group
• They can recognize, communicate, and/or interact with each other
• The instinctive perception of swarms is a group of agents in motion – but that does not always
have to be the case.
• A swarm is better understood if thought of as agents exhibiting a collective behavior
Swarm intelligence (SI):
• An artificial intelligence (AI) technique based on the collective behavior in decentralized, self-
organized systems
• Generally made up of agents who interact with each other and the environment
• No centralized control structures

EXAMPLES OF SWARMS IN NATURE:
Classic example: swarm of bees
• Can be extended to other similar systems:
• Ant colony
• Agents: ants
• Flock of birds
• Agents: birds
• Traffic
• Agents: cars
• Crowd
• Agents: humans
• Immune system
• Agents: cells and molecules

SWARM ROBOTICS
• Swarm robotics
• The application of SI principles to collective robotics
• A group of simple robots that can only communicate locally and operate in a biologically inspired
manner
• A currently developing area of research
Two common SI algorithms
• Ant colony optimization
• Particle swarm optimization
Ant colony optimization (ACO):
• The study of artificial systems modeled after the behavior of real ant colonies and are useful
in solving discrete optimization problems
• Introduced in 1992 by Marco Dorigo
• Originally called it the Ant System (AS)
• Has been applied to
• Traveling salesman problem (and other shortest path problems)
• Several np-hard problems
• It is a population-based metaheuristic used to find approximate solutions to difficult

WHAT IS METAHEURISTIC?
• “A metaheuristic refers to a master strategy that guides and modifies other heuristics to
produce solutions beyond those that are normally generated in a quest for local
optimality” – Fred Glover
• Or more simply:
• It is a set of algorithms used to define heuristic methods that can be used for a large set of
problems
Artificial ants
• A set of software agents
• Stochastic
• Based on the pheromone model
• Pheromones are used by real ants to mark paths. Ants follow these paths (I.E., Trail-following
behaviors)
• Incrementally build solutions by moving on a graph
• Constraints of the problem are built into the heuristics of the ants

USING ACO
• The optimization problem must be written in the form of a path finding problem with a
weighted graph
• The artificial ants search for “good” solutions by moving on the graph
• Ants can also build infeasible solutions – which could be helpful in solving some optimization
problems
• The metaheuristic is constructed using three procedures:
• Construct ants solutions
• Update pheromones
• Daemon actions
CONSTRUCT ANTS SOLUTIONS
• Manages the colony of ants
• Ants move to neighboring nodes of the graph
• Moves are determined by stochastic local decision policies based on pheromone tails and
heuristic information
• Evaluates the current partial solution to determine the quantity of pheromones the ants

Update pheromones
• Process for modifying the pheromone trails
• Modified by
• Increase
• Ants deposit pheromones on the nodes (or the edges)
• Decrease
• Ants don’t replace the pheromones and they evaporate
• Increasing the pheromones increases the probability of paths being used (I.E., Building
the solution)
• Decreasing the pheromones decreases the probability of the paths being used (I.E.,
Forgetting)
Daemon actions
• Used to implement larger actions that require more than one ant
• Examples:
• Perform a local search
• Collection of global information

PARTICLE SWARM OPTIMIZATION (PSO)
• A population based stochastic optimization technique
• Searches for an optimal solution in the computable search space
• Developed in 1995 by dr. Eberhart and dr. Kennedy
• Inspiration: swarms of bees, flocks of birds, schools of fish
• In PSO individuals strive to improve themselves and often achieve this by observing and
imitating their neighbors
• Each PSO individual has the ability to remember
• PSO has simple algorithms and low overhead
• Making it more popular in some circumstances than genetic/evolutionary algorithms
• Has only one operation calculation:
• Velocity: a vector of numbers that are added to the position coordinates to move an individual

PSYCHOLOGICAL SYSTEMS
• A psychological system can be thought of as an “information-processing” function.
• You can measure psychological systems by identifying points in psychological space.
• Usually the psychological space is considered to be multidimensional.
“Philosophical leaps” required:
• Individual minds = a point in space
• Multiple individuals can be plotted in a set of coordinates
• Measuring the individuals result in a “population of points”
• Individuals near each other imply that they are similar
• Some areas of space are better than other

APPLYING SOCIAL PSYCHOLOGY
• Individuals (points) tend to
• Move towards each other
• Influence each other
• Why?
• Individuals want to be in agreement with their neighbors
• Individuals (points) are influenced by:
• Their previous actions/behaviors
• The success achieved by their neighbors
22

WHAT HAPPENS IN PSO
• Individuals in a population learn from previous experiences and the experiences of those
around them
• The direction of movement is a function of:
• Current position
• Velocity (or in some models, probability)
• Location of individuals “best” success
• Location of neighbors “best” successes
• Therefore, each individual in a population will gradually move towards the “better” areas
of the problem space
• Hence, the overall population moves towards “better” areas of the problem space.

PERFORMANCE OF PSO ALGORITHMS:
 Relies on selecting several parameters correctly
 Parameters:
 Constriction factor
 Used to control the convergence properties of a PSO
 Inertia weight
 How much of the velocity should be retained from previous steps
 Cognitive parameter
 The individual’s “best” success so far
 Social parameter
 Neighbors’ “best” successes so far
 Vmax
 Maximum velocity along any dimension

ADVANTAGES OF SI
• The systems are scalable because the same control architecture can be
applied to a couple of agents or thousands of agents
• The systems are flexible because agents can be easily added or removed
without influencing the structure
• The systems are robust because agents are simple in design, the reliance
on individual agents is small, and failure of a single agents has little impact
on the system’s performance
• The systems are able to adapt to new situations easily

AGENTS VS SOFTWARE PROGRAMS
• Traditional software programs lack the ability to assess and react to the environment
and modify their behaviour accordingly.
• They do not follow a goal-oriented and autonomous approach to problem- solving.
• In such program there is no concept of capturing environment which is dynamic in
nature and can effects the output of the system at different times of its invocation
• We will try to understand this using following approach:
 AGENTS & OBJECTS
 AGENTS & EXPERT SYSTEMS

 AGENTS & OBJECTS :
• Wooldridge (American professor) has depicted the underlying difference between agents and
objects in terms of autonomy and behaviour which depends on characteristics such as reaction,
proactiveness and social ability.
• Standard object models do not support the kind of behaviour normally displayed by agents.
• The most basic difference lies in the degree to which agents & objects are autonomous.
• The manner in which different objects communicate with each other is called message passing,
example of a typical object that is clearly defined in Java/ C++ consist of instance variables and
methods which can have public or private accesses.
• On other hand, in case of an agent, it may or may not chose to perform a certain action which is
of no interest to itself even if it is directed by other agents in favour of that particular action.
• That is if agent 1 request agent 2 to perform an action, then agent 2 may choose to perform or
not perform this action. Therefore, the decision to perform a given action rests with agent.
• In the case of object systems the decision is taken by the object that invokes the method.
• Thus from above analysis, we can state that agents display stronger sense of autonomy than
objects, and can take important decision of whether or not to perform an action on the request
of another agent.
27

AGENTS & EXPERT SYSTEMS:
• Expert systems where considered to be the most important AI technology of 1980’s.
• An expert system is a system that is considered to be an expert system when it comes to
solving problems or giving advice in some knowledge- rich domain.
• Expert system are defined as rule- based systems in which a knowledge engineer uses the
knowledge of a certain domain and codes this knowledge as rules and facts in a special
type of database known as a knowledge base.
• The rules are usually rules of thumb, that is, they are based on some heuristics knowledge
of the domain expert.
• Therefore, expert systems are based on the fact that pervious knowledge of a certain
application exists and that we can acquire this knowledge from samples or interviews with
the domain experts and then code this gathered knowledge into the knowledge base.
• However, expert systems are not capable of interacting with their environment and do no
display reactive, proactive or social abilities such as cooperation, coordination and
negotiation.
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CLASSIFICATION OF AGENTS
• Agents can be classified into different classes.
• Nwana has defined a typology and classified agents on the basis of the
parameters: mobility and interaction with environment(nwana, 1996).
• They maybe also be further classified depending on the primary attributes such
as autonomy, cooperation and learning ability.
• The term mobility refers to the ability of agents to move around in a given
network.
• Agents that posses mobility are called mobile agents, they are able to roam
around in wide-area networks, interact with foreign hosts and also perform
tasks on behalf of their owners before returning back to the originator.
• We have three primary characteristics of agents are as follows:
Autonomy, Reactivity and Proactiveness
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• AUTONOMY:
- Autonomy is that characteristics of an agent which enables it to function without the
direct intervention of humans or other intelligent systems, and thus retain control over
its actions and internal state.
- Autonomy is considered to be the central concept in designing an agent.
• REACTIVITY:
- Reactivity is that characteristics of agents owing to which they judge their environment
and respond in accordance to the changes occurring in it.
- As mentioned earlier, reactive agents do not possess any internal model of their
environment and act using a response type of behaviour by responding to the
environment in which they are embedded.
• PROACTIVENESS:
- Agent that posses the characteristics of proactiveness are capable of taking initiative to
make decisions in order to achieve their goals.

WORKING OF AN AGENT
• AN AGENT GENERALLY MAPS ITS INTERNALS STATE TO ITS DATA STRUCTURES, THE
OPERATIONS WHICH MAY BE PERFORMED ON THESE DATA STRUCTURES, AND THE
CONTROL FLOW BETWEEN THE DATA STRUCTURES.
• ONE OF THE CHALLENGING GOALS TO DESIGN AN AGENT PROGRAM IS TO IMPLEMENT
THE MAPPING FROM PERCEPT'S TO ACTIONS.
• THE AGENTS TAKES SENSORY INPUT FROM THE ENVIRONMENT AND PRODUCES ACTIONS
THAT AFFECT IT AS OUTPUT.
• THE AGENT STARTS IN SOME INITIAL INTERNAL STATE, OBSERVES ITS ENVIRONMENT
STATES, AND THEN GENERATES A PERCEPT.
• BASED ON THIS PERCEPT, THE ACTION IS THEN PERFORMED, AND THE AGENT ENTERS
ANOTHER CYCLE, UPDATING IT’S STATE AND CHOOSING AN ACTION TO PERFORM.

CATEGORIES OF AGENTS

SINGLE AGENT AND MULTIAGENT SYSTEMS
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ARCHITECTURE OF INTELLIGENT AGENTS
• We will look into four types of agents architectures:
- Logical based,
- Reactive,
- Belief- desire- intention,
- Layered.
• Each of this is described as given below:
- Logical based architecture : agents in which decision making is done through logical
deduction
- Reactive architecture: agents in which decision making is implemented in some form of direct
mapping from situation to action.
- Belief- desire- intention architecture: agents in which decision making depends upon the
manipulation of data structures representing the beliefs, desires and intentions of the agent.
- Layered architecture: agents in which decision making is done via various software layers,
each of which is more or less explicitly reasoning about the environment at different levels of
abstraction.

Reactive Architecture

APPLICATIONS

Applied Artificial Intelligence Unit 4 Semester 3 MSc IT Part 2 Mumbai University

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