experiment journal of artificial intelligence of criminal story PDF

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Criminal Story mystery solving using ptolog...

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Batch: A4

Roll No.:

1911055

Experiment / assignment / tutorial No. 3 Grade: AA / AB / BB / BC / CC / CD /DD

Signature of the Staff In-charge with date

Title: Implementation of Goal based agent architecture using PROLOG. ______________________________________________________________________ Objective: To use the concepts of knowledge engineering to design and solve moderate complex problem. ______________________________________________________________________ Expected Outcome of Experiment: Course Outcom e CO1

After successful completion of the course students should be able to

Understand the history & various application of AI and choose appropriate agent architecture to solve the given problem.

______________________________________________________________________ Books/ Journals/ Websites referred: 1. https://www.csupomona.edu/~jrfisher/www/prolog_tutorial/contents.html 2. http://www.csupomona.edu/~jrfisher/www/prolog_tutorial/pt_framer.html 3. http://www.doc.gold.ac.uk/~mas02gw/prolog_tutorial/prologpages/ 4. http://classes.soe.ucsc.edu/cmps112/Spring03/languages/prolog/PrologIntro.pdf 5. “Prolog: Programming for Artificial Intelligence” by Ivan Bratko, Pearson education Publications 6. “Artificial Intelligence: a Modern Approach” by Russel and Norving, Pearson education Publications 7. “Artificial Intelligence” By Rich and knight, Tata Mcgraw Hill Publications ______________________________________________________________________ Pre Lab/ Prior Concepts: [Type text]

K. J. Somaiya College of Engineering, Mumbai-77 Agents, Agent Architecture, Programming with PROLOG ______________________________________________________________________ Historical Profile: Knowledge is vast, uncertain and continuously changing. These properties of knowledge make it difficult to arrive at a result. A murder mystery is a kind of situation which depicts the uncertain nature of knowledge and also emphasizes the need of choosing right clauses from entire knowledgebase to make a decision. He goal based agent architecture and some knowledge engineering can help in solutioning of such problems. The logical agents are complex but they can reason and learn from the actions and new precepts. They are less like acting and think like humans but more like acting and thinking rational agents. Knowledge and reasoning play a crucial role in dealing with partially observable environments. A knowledge based agent can combine the general knowledge with current percept to infer the hidden aspects of the current state prior to selecting actions. _____________________________________________________________________ New Concepts to be learned: Knowledge engineering, implementing complex agent architecture, uncertainty in knowledge. _____________________________________________________________________ The Knowledge Engineering Process 1. Identify the task 2. Assemble the relevant knowledge 3. Decide on vocabulary of predicates, functions and constants 4. Encode general knowledge about the domain 5. Encode description of specific problem instance 6. Pose queries to the inference procedure and get answers 7. Debug the knowledge base Algorithm for KB-Agent:

Somaiya Vidyavihar University

2021-22 Batch

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K. J. Somaiya College of Engineering, Mumbai-77 Problem Statement: In the middle of last winter, eight guests were invited to a luxurious retreat at the Duke of York Grand Hotel. On the last day of their three-day getaway, the guests were free to vacate to their own occupations. Mrs White and Reverend Green did some “gardening” walking alongside the water fountains, Colonel Mustard and Professor Plum played golf (alone though, purposefully avoiding each other). The other guests spent their days either in their rooms or in the lounge, by the log fire. Later on in the afternoon, all the guests were indoors and Colonel Mustard was seen playing cards with Reverend Green and Mrs Peacock. As the guests were called for dinner, they soon realised that Dr Black was missing. He was later found lying down on the floor of his bedroom. Dr Black had been shot dead using an old fashion revolver. Except from a few muddy footprints at the entrance of his bedroom, there was no other evidence left by the murderer. Here is the list of all the guests for the weekend and the rooms they were staying in. Note that the hotel consists of twin bedrooms accommodating two guests per room. We also know that three of the guests (Reverend Green, Colonel Mustard and Madame Rose) own a revolver that they brought with them and kept in their room.

Knowledge Engineering steps applied to chosen problem: guest(dr_black). guest(reverend_green). guest(miss_scarlett). guest(colonel_mustard). guest(prof_plum). Somaiya Vidyavihar University

2021-22 Batch

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K. J. Somaiya College of Engineering, Mumbai-77 guest(mrs_peacock). guest(madame_rose). guest(mrs_white). victim(dr_black). smoker(dr_black). smoker(miss_scarlett). smoker(colonel_mustard). smoker(mrs_peacock). smoker(mrs_white). played_golf(colonel_mustard). played_golf(prof_plum). gardening(mrs_white). gardening(reverend_green). played_cards(reverend_green). played_cards(colonel_mustard). played_cards(mrs_peacock). room(dr_black,22). room(reverend_green,24). room(miss_scarlett,21). room(colonel_mustard,24). room(prof_plum,22). room(mrs_peacock,23). room(madame_rose,21). room(mrs_white,23). has_revolver(reverend_green). has_revolver(colonel_mustard). has_revolver(madame_rose). %Rules roommates(X,Y):-guest(X),guest(Y),room(X,Z),room(Y,Z).%,X @> Y. revolver_access(X):- guest(X),has_revolver(X). %these guests brought revolver revolver_access(X):-roommates(Z,X),has_revolver(Z). %roommate of the guests who bought revolver got_muddy(X):- smoker(X). %might have went outside for smoking got_muddy(X):-gardening(X). %very obvious reason for getting muddy got_muddy(X):-played_golf(X). %might have got dirty not_suspect(X):- played_cards(X). %at the time of murder they were seen publicly not_suspect(X):- not(revolver_access(X)). %murder weapon is revolver

Somaiya Vidyavihar University

2021-22 Batch

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K. J. Somaiya College of Engineering, Mumbai-77 suspects(X):-got_muddy(X),revolver_access(X),not(victim(X)). guilty(X):-suspects(X), not(not_suspect(X)).

Agent Architecture (Justify the blocks): Doing the PEAS analysis of the crime scene we get the following information: Performance measure: Maximizing chances of finding the guilty person by narrowing down the suspects, Minimizing chances of wrong conviction by finding the innocents. Environment: Hotel room, floor Actuators: Muddy shoes, revolvers Sensors: Camera, Smoke detectors

Team Members: 1. Ayush Singh 2. Rushabh Shah

Post Lab Objective Questions

1. Which is not a Goal-based agent? a. Inference b. Search c. Planning d. Conclusion e. Dynamic search. Answer: d. Conclusion 2. Which were built in such a way that humans had to supply the inputs and interpret the outputs? a. Agents b. Sensor c. AI System d. Actuators Answer: c. AI system

Somaiya Vidyavihar University

2021-22 Batch

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K. J. Somaiya College of Engineering, Mumbai-77

Post Lab Subjective Questions Explain the role of PEAS and task environment in choosing the agent architecture. Justify your answer with an example. Ans: PEAS System is used to categorize similar agents together. The PEAS system delivers the performance measure with respect to the environment, actuators and sensors of the respective agent. Most of the highest performing agents are Rational Agents. Rational Agent: The rational agent considers all possibilities and chooses to perform the highly efficient action. For example, it chooses the shortest path with low cost for high efficiency. PEAS stands for Performance measure, Environment, Actuator, Sensor. 1. Performance Measure: Performance measure is the unit to define the success of an agent. Performance varies with agents based on their different precept. 2. Environment: Environment is the surrounding of an agent at every instant. It keeps changing with time if the agent is set in motion. There are 5 major types of environments:  Fully Observable & Partially Observable  Episodic & Sequential  Static & Dynamic  Discrete & Continuous  Deterministic & Stochastic 3. Actuator: Actuator is a part of the agent that delivers the output of an action to the environment. 4. Sensor: Sensors are the receptive parts of an agent which takes in the input for the agent. Let's suppose a self-driving car then PEAS representation will be: Performance: Safety, time, legal drive, comfort Environment: Roads, other vehicles, road signs, pedestrian Actuators: Steering, accelerator, brake, signal, horn Sensors: Camera, GPS, speedometer, odometer, accelerometer, sonar.

Somaiya Vidyavihar University

2021-22 Batch

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