First-order Predicate Calculus

Lecture - 9 First Order Logic

Order: Reorder
Duration: 59:50
Updated: 02 Aug 2013

Lecture Series on Artificial Intelligence by Prof. P. Dasgupta, Department of Computer Science & Engineering, I.I.T,kharagpur. For More details on NPTEL visi...

http://wn.com/Lecture_9_First_Order_Logic

Predicate Logic Symbolization Summary

Order: Reorder
Duration: 9:53
Updated: 13 Aug 2013

Summary of the basic symbolization forms for predicate logic.

http://wn.com/Predicate_Logic_Symbolization_Summary

Unit 7 3 First Order Logic

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Duration: 3:43
Updated: 15 Aug 2013

Unit 7 3 First Order Logic.

http://wn.com/Unit_7_3_First_Order_Logic

8.1 Predicate Logic: Symbols & Translation

Order: Reorder
Duration: 57:48
Updated: 10 Aug 2013

Professor Thorsby introduces the key elements of predicate logic for translation & symbolization.

http://wn.com/81_Predicate_Logic_Symbols_Translation

Semantics and Pragmatics - Predicate Logic I

Order: Reorder
Duration: 15:28
Updated: 09 Jul 2013

This first E-Lecture on Predicate Logic is meant as a gentle introduction. It first points out why propositional logic alone is not sufficient for the formal...

http://wn.com/Semantics_and_Pragmatics_Predicate_Logic_I

Inference in First order logic: Inference rule and unification

Order: Reorder
Duration: 18:00
Updated: 30 Aug 2013

http://wn.com/Inference_in_First_order_logic_Inference_rule_and_unification

first-order resolution refutation proofs

Order: Reorder
Duration: 12:15
Updated: 14 Apr 2013

UNH CS 730.

http://wn.com/first-order_resolution_refutation_proofs

PHIL 470: Online Lecture 2 First Order Logic

Order: Reorder
Duration: 17:26
Updated: 02 Aug 2013

http://wn.com/PHIL_470_Online_Lecture_2_First_Order_Logic

Predicate Calculus I

Order: Reorder
Duration: 6:57
Updated: 17 Jul 2013

intro and setup for predicate calculus examples.

http://wn.com/Predicate_Calculus_I

Mod-01 Lec-22 First - Order Theories

Order: Reorder
Duration: 55:51
Updated: 29 Apr 2013

Logic for CS by Dr. S. Arun Kumar, Department of Computer Science and Engineering, IIT Delhi. For more details on NPTEL visit http://nptel.iitm.ac.in.

http://wn.com/Mod-01_Lec-22_First_Order_Theories

The G Proof Video 4: Crash Course in Predicate Logic, Part A: Quantifiers

Order: Reorder
Duration: 61:49
Updated: 26 Jun 2013

Short Synopsis of Video 4: Predicate Logic is built on Sentential Logic. But Predicate Logic goes further by looking inside of sentences. This Video 4 is the...

http://wn.com/The_G_Proof_Video_4_Crash_Course_in_Predicate_Logic,_Part_A_Quantifiers

Chapter 8. First Order Logic - Syntax and Semantics

Order: Reorder
Duration: 58:00
Updated: 27 Jul 2013

Chapter 8. First Order Logic - Syntax and Semantics, In Artificial Intelligence for Undergraduate Students, in BZU, Spring 2013.

http://wn.com/Chapter_8_First_Order_Logic_Syntax_and_Semantics

Logic: Rules Of Validity, Sentence Translation (Mac Deaver) (5 of 9)

Order: Reorder
Duration: 37:34
Updated: 17 Oct 2013

http://wn.com/Logic_Rules_Of_Validity,_Sentence_Translation_Mac_Deaver_5_of_9

Scape One - 02 First Order Logic

Order: Reorder
Duration: 6:34
Updated: 19 Oct 2012

Scape One - 11.11.11 Track 02 - First Order Logic.

http://wn.com/Scape_One_02_First_Order_Logic

LO27: Natural Deduction in Predicate Logic

Order: Reorder
Duration: 10:43
Updated: 21 May 2013

http://wn.com/LO27_Natural_Deduction_in_Predicate_Logic

Mod-01 Lec-18 The Semantic of Predicate Logic

Order: Reorder
Duration: 50:32
Updated: 13 Dec 2012

Logic for CS by Dr. S. Arun Kumar, Department of Computer Science and Engineering, IIT Delhi. For more details on NPTEL visit http://nptel.iitm.ac.in.

http://wn.com/Mod-01_Lec-18_The_Semantic_of_Predicate_Logic

first-order logic

Order: Reorder
Duration: 11:32
Updated: 09 Jul 2013

UNH CS 730.

http://wn.com/first-order_logic

Logic & Language - quantifiers & bound variables (Logic 3 of 5)

Order: Reorder
Duration: 7:38
Updated: 07 Aug 2013

This video introduces quantifiers, including the universal quantifier "all", the existential quantifier "some" and the negative "no"/"not". You will also lea...

http://wn.com/Logic_Language_quantifiers_bound_variables_Logic_3_of_5

Generating common sense knowledge (pt 7) using Human Level Artificial Intelligence

Order: Reorder
Duration: 4:27
Updated: 03 Aug 2013

This video shows a robot generating common sense when observing events. There are no sound in the video because I wanted to show the viewers what the robot i...

http://wn.com/Generating_common_sense_knowledge_pt_7_using_Human_Level_Artificial_Intelligence

Drawing a picture by copying using Human Level Artificial Intelligence

Order: Reorder
Duration: 5:16
Updated: 25 Aug 2013

This video shows a robot drawing a picture by copying real-life objects. There are no sound in the video because I wanted to show the viewers what the robot is thinking while making a drawing. The flashing text and freeze frames are the internal thoughts of the robot and not instruction text for the viewers. These internal thoughts describe the details of how the robot produce intelligence. My robot doesn't use: planning programs/heuristic searches (used by MIT and Stanford University), Bayesian's probability theories for decision making, Bayesian's equation for induction and deduction, semantic networks for natural language understanding, predicate calculus, common sense systems, first-order logic, rule-based systems, genetic programming, or MACHINE LEARNING. By the way, you can't use current AI methods to build a robot that can draw a comic book. For example, the IBM Watson can't be used to draw comic books. Those researchers can spend 50 years programming instructions into the Watson, their code foundation disallows their robot to draw a comic book (or do complex human tasks). The robot learns to draw pictures through a bootstrapping process. If the robot wants to be a comic book artist, he needs to learn art skills gradually. There are actually 3 videos made on this series. 1. Drawing a picture by copying. 2. Drawing a picture from memory. 3. Making a comic book page. Each video shows the gradual learning the robot must go through in order to become a comic book artist. I want you to recall all the art skills that you learned from school, from Kindergarten to college. First, teachers teach students to draw by copying real life objects. The teacher would put an object, like a doll, on a table and asks the students to copy what they see. The teacher would tell students to look at shapes and edges and draw lines on paper to represent that object. Lessons about what to look at, where to look at, and what lines to draw on paper are facts the teacher teaches the students. Practicing drawing different objects is part of the learning process. Over the years, the students' brain uses personal likes/dislikes to develop their own style of drawing. Once the student can draw pictures by copying them, then he is ready for the next lesson. The second lesson taught by teachers is to draw a picture based on an image from your mind. This lesson is very similar to the first lesson. Instead of copying an object from real life, the student is copying an image from memory. The student extracts an image of something he wants to draw. Next, he looks at this image in his mind and copies it. The previous 2 lessons basically builds on itself to become more complex. The third lesson is to draw pictures based on storytelling. Learning the knowledge to become a comic book artist is a very complicated thing. First, the student has to know how to draw all objects, this includes: human anatomy, cities, landscapes, cars, highways, tables, animals, super heroes, faces, body parts, things, and places. The student has to have the ability to draw "all" objects and from "any" 360 degree angle. The student needs to have talent in his artwork, which means that his drawings have to appeal to a large group of people. Next, the student has to have knowledge about movie directing. He has to have a movie playing in his head and the student decides which scenes to show on paper. The comic book artist usually works with a writer. The writer provides a one page script to the artist. The artist will read the script and design the page panels. In each panel, the artist has to decide what camera angle to use, what objects will be in each panel, where to put the captions and so forth. There are books written on how to make comicbooks and the rules are vast. The knowledge to be a comic book artist is massive and sometimes it takes 10-20 years to master. These 3 videos i'm showing people demonstrates how the robot learns drawing skills through a bootstrapping process, whereby knowledge is built on top of each other to create complex thinking. First the robot learns to draw by copying. Next, the robot learns to draw from memory. Finally, when the robot learned to draw all objects and events, then he can learn to draw a comicbook. Drawing a comic book includes knowing knowledge about perspective, shading, composition, angle shots, realism, anatomy drawing, landscape drawing, environment object drawing, linear story telling, and so forth. As stated before, most knowledge for drawing is based on practice. Practicing more will mean better skills for the robot. For more information about human level artificial intelligence, visit my website: http://www.humanlevelartificialintelligence.com

http://wn.com/Drawing_a_picture_by_copying_using_Human_Level_Artificial_Intelligence

Playing Michael Jackson's Moonwalker using Human Level Artificial Intelligence

Order: Reorder
Duration: 13:16
Updated: 13 Sep 2013

This video shows a robot playing Moonwalker. There are no sound in the video because I wanted to show the viewers what the robot is thinking while playing the game. The flashing text and freeze frames are the internal thoughts of the robot and not instruction text for the viewers. These internal thoughts describe the details of how the robot produce intelligence. My robot doesn't use: planning programs/heuristic searches (used by MIT and Stanford University), Bayesian's probability theories for decision making, Bayesian's equation for induction and deduction, semantic networks for natural language understanding, predicate calculus, common sense systems, first-order logic, rule-based systems, genetic programming, or MACHINE LEARNING. Moonwalker is a game where the player has to navigate in an unknown environment to rescue children. This robot is playing this level for the first time and has no idea what the level environment looks like. He uses logic to plan out optimal routes to travel to do his searches. The player (the robot) will try not to travel in the same areas twice. The goal is to rescue the children in the fastest time possible. Along the way, the player encounters enemies. In order to avoid getting hit or mobbed by a gang, the robot has to kill enemies. Avoiding enemies is a stupid move because they can always turn around and follow you. By killing the enemy it eliminates the threat all together. Also, the game is programmed so that enemies are consistently attacking the player. In order to survive, the player has to kill enemies. If he doesn't, the enemies will keep coming and gang up on the player, resulting in death. In the video, the robot is actually doing multiple tasks at the same time: killing enemies, rescuing children, and navigating in an unknown environment. He is making decisions every second that will satisfy all 3 tasks. Sometimes he might do 2 tasks or 1 task, and it really depends on the current situation. Other times, he might abandon a committed action/s and generate a new action. The robot will always pursue actions that will accomplish his goals. In this gameplay, the environment is broken up into 4 levels. The main level, level2, level3, and level4. In the robot's mind he has to visualize all the levels and where these levels are in 3-d space. In each level there are teleporting pods located in various places. The robot has to understand where each teleporting pod takes him. The robot does this by playing the game, roaming around, and testing where certain pods take him. For a medium sized environment like this, it is easy for the robot to use his mind to map out the environment. However, when playing a game like Metroid where the environment is massive, the robot needs a map to identify the environment. Thus, this robot is able to play any navigation game, regardless of how complex the environment is. In games like Metroid or Zelda, the environment is endless and maps are available to players. In order to navigate in such a complex environment, the robot has to generalize information on a map, write mental notes on the map, zoom in on certain areas on the map, and use logic to identify places in the map. The more recent versions of Zelda have multiple layers of a map. For example, a map can have 2-3 different worlds. In each world, people, places, and things are different. The robot has to manage this complex environment in his head. The same goes with 3-d complex environments. The robot simply has to interpret the environment as 3-dimensional and it has an added up or down dimension. For more information about Human Level Artificial Intelligence visit my website: http://www.humanlevelartificialintelligence.com

http://wn.com/Playing_Michael_Jackson's_Moonwalker_using_Human_Level_Artificial_Intelligence

Robot navigating in a complex maze using large maps (pt 1)

Order: Reorder
Duration: 14:50
Updated: 28 Sep 2013

http://www.humanlevelartificialintelligence.com This video shows a robot navigating a complex maze using large maps. There are no sound during parts of the video because I wanted to show the viewers what the robot is thinking while playing the game. The flashing text and freeze frames are the internal thoughts of the robot and not instruction text for the viewers. These internal thoughts describe the details of how the robot produce intelligence. My robot doesn't use: planning programs/heuristic searches (used by MIT and Stanford University), Bayesian's probability theories for decision making, Bayesian's equation for induction and deduction, semantic networks for natural language understanding, predicate calculus, common sense systems, first-order logic, rule-based systems, genetic programming, or MACHINE LEARNING. In games like Metroid or Castlevania or Zelda, the player has to navigate in a massive environment. Maps are provided to the player so that he doesn't get lost. In this video, i'm trying to demonstrate how the robot thinks while navigating in a large environment using maps. As you can see, the robot is analyzing the map, making personal observations, debating where to go, making decisions on how to do searches and so forth. At the same time, he is mentally remembering general or detailed parts of the map to do his navigation. Navigation in an unknown environment is just one small task the robot has to do. A real robot with human level intelligence can do multiple tasks simultaneously. He can: 1. navigate in an unknown environment 2. kill/avoid enemies. 3. follow rules of the game. 4. do recursive goals. 5. use logic to determine next goals. 6. use common sense to solve problems. 7. generate ideas to solve problems. 8. predict the future for selective objects. 9. generate strategies to defeat multiple enemies. 10. manage multiple tasks at the same time. 11. find optimal routes to travel so that repeated travel is brought to a minimal. In one scene, the player was stuck and didn't know what to do. He needed to raise the elevator in order to go to the top level. The robot tried 3 ideas before solving the problem. This ability to generate ideas to solve problems is very important to human intelligence. It shows the robot can adapt and change so that a problem can be solved.

http://wn.com/Robot_navigating_in_a_complex_maze_using_large_maps_pt_1

Playing joust using Artificial Intelligence

Order: Reorder
Duration: 11:02
Updated: 03 Dec 2013

http://www.humanlevelartificialintelligence.com This video shows a robot playing Joust (arcade version). There are no sound in the video because I wanted to show the viewers what the robot is thinking while playing a 2-d arcade game. The flashing text and freeze frames are the internal thoughts of the robot and not instruction text for the viewers. These internal thoughts describe the details of how the robot produce intelligence. My robot doesn't use: planning programs/heuristic searches (used by MIT and Stanford University), Bayesian's probability theories for decision making, Bayesian's equation for induction and deduction, semantic networks for natural language understanding, predicate calculus, common sense systems, first-order logic, rule-based systems, decision trees, genetic programming, or MACHINE LEARNING. In the video, the robot is playing Joust for the first time and has no idea what the rules and objectives of the game is. He uses common sense and logic to discover the rules and objectives of the game. During the playing of the game, the robot has to formulate good strategies to beat the game. There might be obstacles in the game and the robot has to be aware of these obstacles and generate a plan to avoid them. After the robot plays the game several times, he has all the knowledge he needs to replay the game and pass it. This knowledge includes: 1. the objectives and rules of the game. 2. the controls of the game. 3. good strategies to beat the game. 4. identifying obstacles and using logic to avoid them. 5. acquire optimal navigation skills to get to enemies or to move in the game.

http://wn.com/Playing_joust_using_Artificial_Intelligence

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Related Topics

Lecture - 9 First Order Logic

Predicate Logic Symbolization Summary

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Semantics and Pragmatics - Predicate Logic I

Inference in First order logic: Inference rule and unification

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PHIL 470: Online Lecture 2 First Order Logic

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Mod-01 Lec-22 First - Order Theories

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Lecture - 9 First Order Logic

Predicate Logic Symbolization Summary

Unit 7 3 First Order Logic

8.1 Predicate Logic: Symbols & Translation

Semantics and Pragmatics - Predicate Logic I

Inference in First order logic: Inference rule and unification

first-order resolution refutation proofs

PHIL 470: Online Lecture 2 First Order Logic

Predicate Calculus I

Mod-01 Lec-22 First - Order Theories

The G Proof Video 4: Crash Course in Predicate Logic, Part A: Quantifiers

Chapter 8. First Order Logic - Syntax and Semantics

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