The document summarizes a session on planning in artificial intelligence. It discusses classical planning approaches like state-space search and planning graphs. It also covers planning and acting in real-world domains with aspects like time, schedules, and resources. Finally, it describes different planning algorithms like situation-space search with progression and regression approaches.
In this webinar you learn some of the techniques and resources that can help you bolster your Spatial Data Science skills. You can watch the recorded webinar at: https://go.carto.com/webinars/spatial-expert-recorded
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete RecordAsst.prof M.Gokilavani
LIST OF EXPERIMENTS:
1. Implement simple vector addition in Tensor Flow.
2. Implement a regression model in Keras.
3. Implement a perception in TensorFlow/Keras Environment.
4. Implement a Feed Forward Network in TensorFlow/Keras.
5. Implement an image classifier using CNN in TensorFlow/Keras.
6. Improve the deep Learning model by fine tuning hyper parameters.
7. Implement a Transfer Learning concept in image classification.
8. Using a pre trained model on Keras for transfer learning.
9. Perform Sentimental Analysis using RNN.
10. Implement an LSTM based Auto encoding inTensorflow/Keras.
11. Image generation using GAN.
ADDITIONAL EXPERIMENTS
12. Train a deep Learning model to classify a given image using pre trained model.
13. Recommendation system from sales data using Deep Learning.
14. Implement Object detection using CNN.
15. Implement any simple Reinforcement Algorithm for an NLP problem.
In this webinar you learn some of the techniques and resources that can help you bolster your Spatial Data Science skills. You can watch the recorded webinar at: https://go.carto.com/webinars/spatial-expert-recorded
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
CCS335 _ Neural Networks and Deep Learning Laboratory_Lab Complete RecordAsst.prof M.Gokilavani
LIST OF EXPERIMENTS:
1. Implement simple vector addition in Tensor Flow.
2. Implement a regression model in Keras.
3. Implement a perception in TensorFlow/Keras Environment.
4. Implement a Feed Forward Network in TensorFlow/Keras.
5. Implement an image classifier using CNN in TensorFlow/Keras.
6. Improve the deep Learning model by fine tuning hyper parameters.
7. Implement a Transfer Learning concept in image classification.
8. Using a pre trained model on Keras for transfer learning.
9. Perform Sentimental Analysis using RNN.
10. Implement an LSTM based Auto encoding inTensorflow/Keras.
11. Image generation using GAN.
ADDITIONAL EXPERIMENTS
12. Train a deep Learning model to classify a given image using pre trained model.
13. Recommendation system from sales data using Deep Learning.
14. Implement Object detection using CNN.
15. Implement any simple Reinforcement Algorithm for an NLP problem.
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdfAsst.prof M.Gokilavani
UNIT I INTRODUCTION
Neural Networks-Application Scope of Neural Networks-Artificial Neural Network: An IntroductionEvolution of Neural Networks-Basic Models of Artificial Neural Network- Important Terminologies of
ANNs-Supervised Learning Network.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
1. ARTIFICAL INTELLIGENCE
(R18 III(II Sem))
Department of computer science and
engineering (AI/ML)
Session 26
by
Asst.Prof.M.Gokilavani
VITS
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2. TEXTBOOK:
• Artificial Intelligence A modern Approach, Third
Edition, Stuart Russell and Peter Norvig, Pearson
Education.
REFERENCES:
• Artificial Intelligence, 3rd Edn, E. Rich and K.Knight
(TMH).
• Artificial Intelligence, 3rd Edn, Patrick Henny
Winston, Pearson Education.
• Artificial Intelligence, Shivani Goel, Pearson
Education.
• Artificial Intelligence and Expert Systems- Patterson,
Pearson Education.
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3. Topics covered in session 26
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Planning
Classical Planning: Definition of Classical Planning,
Algorithms for Planning with State-Space Search,
Planning Graphs, other Classical Planning
Approaches, Analysis of Planning approaches.
Planning and Acting in the Real World: Time,
Schedules, and Resources, Hierarchical Planning,
Planning and Acting in Nondeterministic Domains,
Multi agent Planning.
4. State space Planning
• Searching Strategies
– possible plans
– "initial plan"
– goal node
– The node itself contains all of the information for
determining a solution plan (e.g. sequence of
actions)
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5. Planning with state space search
Planning as Search:
– There are two main approaches to solving planning
problems, depending on the kind of search space
that is explored:
• Situation-space search
• Planning-space search
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6. Situation-Space Search
• In situation space search
– The search space is the space of all possible states
or situations of the world
– Initial state defines one node
– A goal node is a state where all goals in the goal
state are satisfied
– A solution plan is the sequence of actions (e.g.
operator instances) in the path from the start node
to a goal node.
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7. Box Problem (Hill climbing )
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8. Planning-space search
• The search space is the space of all possible
plans
• A node corresponds to a partial plan
• Initially we will specify an "initial plan" which is
one node in this space
• A goal node is a node containing a plan which is
complete, satisfying all of the goals in the goal
state
• The node itself contains all of the information
for determining a solution plan (e.g. sequence
of actions)
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10. Situation Planning Algorithms
• There are 2 approaches to situation-space
planning:
– Progression situation-space planning
– Regression situation-space planning
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11. Progression(Forward) situation-
space planning
• Forward search through the space of states, starting
from the initial state and using the problem’s actions
until the member of goal states are found.
• You can use any search method you like (i.e. BFS,
DFS, A*)
• Since the forward search is prone to exploring unrelated
nodes, and tend to have many state spaces, it has been
regarded as inefficient without the help of accurate
heuristics.
• Disadvantage: huge search space to explore, so
usually very inefficient
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12. Algorithm
1. Start from initial state.
2. Find all operators whose preconditions are
true in the initial state.
3. Compute effects of operators to generate
successor states.
4. Repeat steps #2-#3 until a new state satisfies
the goal conditions.
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13. Regression (Backward) situation-
space
• Backward search through the relevant states, starting
from the goal state, to the initial state using the inverse
of actions.
• Backward-chaining from goal state to initial state
• Regression situation-space planning is usually more
efficient than progression because many operators are
applicable at each state, yet only a small number of
operators are applicable for achieving a given goal
• Hence, regression is more goal-directed than
progression situation- space planning.
• Disadvantage: cannot always find a plan even if one
exists!
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14. Algorithm
1. Start with goal node corresponding to goal to
be achieved
2. Choose an operator that will add one of the
goals
3. Replace that goal with the operator's
preconditions
4. Repeat steps #2-#3 until you have reached the
initial state
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15. Topics to be covered in next session 27
• Heuristics planning
Thank you!!!
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