Special Issue on the 12th IEEE International Conference
... Scholar. IJCINI is well recognized in the fields of computing, artificial intelligence, and computational intelligence, as well as psychology, cognitive science, and brain science. A number of special issues in IJCINI will be organized on cognitive computing, neurocomputing, and computational intell ...
... Scholar. IJCINI is well recognized in the fields of computing, artificial intelligence, and computational intelligence, as well as psychology, cognitive science, and brain science. A number of special issues in IJCINI will be organized on cognitive computing, neurocomputing, and computational intell ...
Introduction to Artificial Intelligence
... playing and winning a game (checkers, chess, etc.) predicting weather based on observing atmospheric conditions autonomously moving in a hard terrain (desert, city, etc.) recognising a human face or emotions proving a mathematical theorem ...
... playing and winning a game (checkers, chess, etc.) predicting weather based on observing atmospheric conditions autonomously moving in a hard terrain (desert, city, etc.) recognising a human face or emotions proving a mathematical theorem ...
Problem Solving and Search
... Problem-Solving Agents function Simple-Problem-Solving-Agent(percept) returns action inputs: p, a percept static: s, an action sequence, initially empty state, a description of current world state g, a goal, initially null problem, a problem formulation state Update-State(state, p) ...
... Problem-Solving Agents function Simple-Problem-Solving-Agent(percept) returns action inputs: p, a percept static: s, an action sequence, initially empty state, a description of current world state g, a goal, initially null problem, a problem formulation state Update-State(state, p) ...
PHILOSOPHY OF ARTIFICIAL INTELLIGENCE Artificial intelligence
... understanding, believing, intending, thinking ...
... understanding, believing, intending, thinking ...
Slide 1
... 1. Evaluate the initial state. If it is the Goal, return otherwise continue. 2. Repeat until a solution is found or there are no new operators left to be applied of current state: a) Select an operator and apply it to produce new state. b) Evaluate the new state. i. If it is a goal, return and quit ...
... 1. Evaluate the initial state. If it is the Goal, return otherwise continue. 2. Repeat until a solution is found or there are no new operators left to be applied of current state: a) Select an operator and apply it to produce new state. b) Evaluate the new state. i. If it is a goal, return and quit ...
How to keep robot/AI under control?
... to help robots develop in a way that will not bring harm to us. Robots without such considerations will have the power to act inhumanely. However with this method lies a contradiction, in that many of our human actions are animalistic at their core. They are not intelligent acts. We have similar beh ...
... to help robots develop in a way that will not bring harm to us. Robots without such considerations will have the power to act inhumanely. However with this method lies a contradiction, in that many of our human actions are animalistic at their core. They are not intelligent acts. We have similar beh ...
disserertation complete 4
... Seventeenth century natural philosophers such as René Descartes, Robert Boyle and Gottfried Wilhelm Leibniz were equally spellbound by such questions, as are modern scientists and engineers. Even if the high technology of the seventeenth century is now antiquated, the present day debate on natural a ...
... Seventeenth century natural philosophers such as René Descartes, Robert Boyle and Gottfried Wilhelm Leibniz were equally spellbound by such questions, as are modern scientists and engineers. Even if the high technology of the seventeenth century is now antiquated, the present day debate on natural a ...
CIS 830: Advanced Topics in Artificial Intelligence KSU When
... • Total weight W = n initially • False positive: W(t+1) < W(t) + n - in worst case, every variable promoted • False negative: W(t+1) < W(t) - n/2 - elimination of a bad variable • 0 < W < n + un - vn/2 v < 2(u + 1) – Number of mistakes: u + v < 3u + 2 = (k log n), Q.E.D. CIS 830: Advanced Topics ...
... • Total weight W = n initially • False positive: W(t+1) < W(t) + n - in worst case, every variable promoted • False negative: W(t+1) < W(t) - n/2 - elimination of a bad variable • 0 < W < n + un - vn/2 v < 2(u + 1) – Number of mistakes: u + v < 3u + 2 = (k log n), Q.E.D. CIS 830: Advanced Topics ...
Paper
... over a sequence of interactions with the environment. the problem of intelligent This model behavior in a microcosm; the agent has a concrete measure of the success of its actions and must plan ahead to maximize its success. Compared to the larger problem of creating a broadly intelligent agent, the ...
... over a sequence of interactions with the environment. the problem of intelligent This model behavior in a microcosm; the agent has a concrete measure of the success of its actions and must plan ahead to maximize its success. Compared to the larger problem of creating a broadly intelligent agent, the ...
Computer Confluence 6/e
... Natural-language processing has come a long way since ELIZA’s early conversations. Computers still can’t pass the Turing test, but they can at least fool people sometimes. ...
... Natural-language processing has come a long way since ELIZA’s early conversations. Computers still can’t pass the Turing test, but they can at least fool people sometimes. ...
marked
... What is a plausible outcome of an action? Related questions How can agents make rational decisions given beliefs about outcomes? What does it mean (algorithmically) to “choose the best”? ...
... What is a plausible outcome of an action? Related questions How can agents make rational decisions given beliefs about outcomes? What does it mean (algorithmically) to “choose the best”? ...
View PDF - Advances in Cognitive Systems
... An example of state-of-the-art robot technology is Google’s self-driving car (Thrun & Urmson, 2011). This is capable of the remarkable feat of driving safely in urban traffic, but to do so requires careful preparation. For example, prior to autonomous driving, the car must be driven around a locatio ...
... An example of state-of-the-art robot technology is Google’s self-driving car (Thrun & Urmson, 2011). This is capable of the remarkable feat of driving safely in urban traffic, but to do so requires careful preparation. For example, prior to autonomous driving, the car must be driven around a locatio ...
Strong Physical Symbol System hypothesis
... SORRY I DON’T KNOW THE WORD ‘STEEPLE’ A ‘steeple’ is a stack that contains two green cubes and a pyramid. I UNDERSTAND Trade you the steeple for three red cubes SORRY I DON’T KNOW THE WORD ‘TRADE’ A ‘trade’ is a free exchange of ownership SORRY I DON’T KNOW THE WORD ‘FREE’ Sorry, I thought you were ...
... SORRY I DON’T KNOW THE WORD ‘STEEPLE’ A ‘steeple’ is a stack that contains two green cubes and a pyramid. I UNDERSTAND Trade you the steeple for three red cubes SORRY I DON’T KNOW THE WORD ‘TRADE’ A ‘trade’ is a free exchange of ownership SORRY I DON’T KNOW THE WORD ‘FREE’ Sorry, I thought you were ...
Embodied artificial intelligence
... AI, and we know what problems such approaches have.3 So EAI is the only way forward”. Others see the list as think “Hey, that’s straw-man AI! There’s lots of AI work that no one would call ‘embodied’ which rejects some, most or all of those points.4 So EAI is not needed; ‘non-embodied’ AI is already ...
... AI, and we know what problems such approaches have.3 So EAI is the only way forward”. Others see the list as think “Hey, that’s straw-man AI! There’s lots of AI work that no one would call ‘embodied’ which rejects some, most or all of those points.4 So EAI is not needed; ‘non-embodied’ AI is already ...
Intelligent OAM
... key component: – 1. Central Controller: Centralized controller is the core component of Network Artificial Intelligence which can be called as 'Network Brain'. It man collect huge data of network states, store the data based on the big data platform, and carry on the machine learning, to achieve net ...
... key component: – 1. Central Controller: Centralized controller is the core component of Network Artificial Intelligence which can be called as 'Network Brain'. It man collect huge data of network states, store the data based on the big data platform, and carry on the machine learning, to achieve net ...
intelligent robots: the question of embodiment
... While some believe that implementing a control paradigm on a physical robot is sufficient for fulfilling the embodiment criteria, Dautenhahn and Christaller [29] argue that this results in a robot not being aware of whether it is acting in a simulated or physical body. They write that the “developme ...
... While some believe that implementing a control paradigm on a physical robot is sufficient for fulfilling the embodiment criteria, Dautenhahn and Christaller [29] argue that this results in a robot not being aware of whether it is acting in a simulated or physical body. They write that the “developme ...
12.2 Definition of Planning
... We now enter the main loop of POP algorithm where we iteratively find any unsatisfied precondition in our existing plan and then satisfying it by an appropriate action. ...
... We now enter the main loop of POP algorithm where we iteratively find any unsatisfied precondition in our existing plan and then satisfying it by an appropriate action. ...
CS 561: Artificial Intelligence CS 561: Artificial Intelligence
... What is AI? The Turing test. Rationality. Branches of AI. Research disciplines connected to and at the foundation of AI. Brief history of AI. Challenges for the future. Overview of class syllabus. ...
... What is AI? The Turing test. Rationality. Branches of AI. Research disciplines connected to and at the foundation of AI. Brief history of AI. Challenges for the future. Overview of class syllabus. ...
Symbolic Processing * CSCE 3210
... 1. Understand Lisp data types, internal functions, advanced Lisp objects and abstractions. 2. Understand the design principles of artificial intelligence. 3. Understand basic principles of Search, two player games and neural networks. 4. Understand one or more application in Artificial Intelligence. ...
... 1. Understand Lisp data types, internal functions, advanced Lisp objects and abstractions. 2. Understand the design principles of artificial intelligence. 3. Understand basic principles of Search, two player games and neural networks. 4. Understand one or more application in Artificial Intelligence. ...
Appendix: Pruning Search Space for Weighted
... Ground Clause: Clauses formed as a result of replacing each variable by all possible constants in each predicate of a clause. Head: Left side of (:-) (if ) is called head of the clause. Herbrand Interpretation: A (Herbrand) interpretation is a truth assignment to all the atoms formed as a result of ...
... Ground Clause: Clauses formed as a result of replacing each variable by all possible constants in each predicate of a clause. Head: Left side of (:-) (if ) is called head of the clause. Herbrand Interpretation: A (Herbrand) interpretation is a truth assignment to all the atoms formed as a result of ...
Special Session on Swarm Intelligence for Global Optimization 2013
... etc. During the past decade, algorithms based on SI have emerged as potential candidates for solving complex and intricate global optimization problems which are otherwise difficult to solve by traditional methods. Some popular SI based techniques for Global Optimization include Ant Colony Optimizat ...
... etc. During the past decade, algorithms based on SI have emerged as potential candidates for solving complex and intricate global optimization problems which are otherwise difficult to solve by traditional methods. Some popular SI based techniques for Global Optimization include Ant Colony Optimizat ...
AAAI-11 / IAAI -11 Exhibitor Information
... AAAI-11 / IAAI -11 Exhibitor Information On behalf of AAAI, we invite you to exhibit at the Twenty-Fifth AAAI Conference on Artificial Intelligence and the Twenty-Third Conference on Innovative Applications of Artificial Intelligence, to be held August 7 - 11, 2011 in San Francisco, California. Each ...
... AAAI-11 / IAAI -11 Exhibitor Information On behalf of AAAI, we invite you to exhibit at the Twenty-Fifth AAAI Conference on Artificial Intelligence and the Twenty-Third Conference on Innovative Applications of Artificial Intelligence, to be held August 7 - 11, 2011 in San Francisco, California. Each ...
Lecture 2 - Artificial Intelligence: Foundations of Computational Agents
... achievement goal is a goal to achieve. This can be a complex logical formula. complex preferences may involve tradeoffs between various desiderata, perhaps at different times. I I ...
... achievement goal is a goal to achieve. This can be a complex logical formula. complex preferences may involve tradeoffs between various desiderata, perhaps at different times. I I ...
01-Introduction
... occurrence of unmodeled events or environments, requiring the system to dynamically adapt and replan. ...
... occurrence of unmodeled events or environments, requiring the system to dynamically adapt and replan. ...
AAAI-08 / IAAI-08 Exhibitor Information
... On behalf of AAAI, we invite you to exhibit at the Twenty-Third AAAI Conference on Artificial Intelligence and the Twentieth Conference on Innovative Applications of Artificial Intelligence, to be held July 13 - 17, 2008 in Chicago, Illinois. Each year the AAAI conference brings together about 1,000 ...
... On behalf of AAAI, we invite you to exhibit at the Twenty-Third AAAI Conference on Artificial Intelligence and the Twentieth Conference on Innovative Applications of Artificial Intelligence, to be held July 13 - 17, 2008 in Chicago, Illinois. Each year the AAAI conference brings together about 1,000 ...