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Showing posts from January, 2019

Week 3 - Muddiest Points

The resolution algorithm was a bit confusing, are there any resources to help understand it better.

Week 4 - Reading

Chapter 13 There are two different ways to deal with the problems arising from incomplete and incorrect information. Conditional planning Execution monitoring. An agent that builds a plan and then executes it while watching for errors is, in a sense, taking into account the possible conditions that constitute execution errors The condition must be known to the agent at that point in the plan. A conditional planner therefore will sometimes create plans that involve carrying out ordinary actions for the purpose of obtaining some needed information. A sensing action can have any number of outcomes. Sensing actions of this type can be used in parameterized plans, where the exact actions to be carried out will not be known until the plan is executed. A second approach is to check the preconditions of each action as it is executed, rather than checking the preconditions of the entire remaining plan. If someone or something else has already changed the world so that the goal i...

Week 3 - Reading

Chapter 7 7.1 The agent maintains a knowledge base, KB, which may initially contain some background knowledge. Then we can expect it to cross the Golden Gate Bridge because it knows that that will achieve its goal. Notice that this analysis is independent of how the taxi works at the implementation level.  This is called the declarative approach to system building. In contrast, the procedural approach encodes desired behaviors directly as program code. 7.2 The wumpus world is a cave consisting of rooms connected by passageways. Lurking somewhere in the cave is the terrible wumpus, a beast that eats anyone who enters its room. Chapter 8 Propositional logic has a third property that is desirable in representation languages, namely, compositionality.  For nondeductive tasks such as learning from experience, outcomes are necessarily dependent on the form of the representations used.  The language of first-order logic, whose syntax and semantics we define ...

Week 2 - Muddiest Point

If more then one solution exists, if it more important for an AI to get the faster answer or the best possible answer?

Week 1 - Muddiest Point

Utility based agents were a bit difficult to grasp, could you explain it with more examples?

Week 2 - Reading

Chapter 3 This chapter talks about various problems that an agent faces and various ways to formulate the problem. It explains different problems based on different scenarios and explains effective ways to come up with a search strategy to solve the problem. It explains the formulate, search, execute design. There are four essentially different types of problems — single state problems, multiple-state problems, contingency problems, and exploration problems. The initial state, operator set, goal test, and path cost function define a problem.  The process of removing detail from a representation is called abstraction. Explains various toy problems and how to solve them. A state is arc-consistent j if every variable has a value in its domain that is consistent with each of the constraints on that I variable.  Chapter 4 This chapter takes the problems from the previous chapter and explains various ways of solving them. It explains various algorithms and ...

Week 1 - Reading

Chapter 1  The definitions on top are concerned with thought processes and reasoning, whereas the ones on the bottom address behavior. The definitions on the left measure success in terms of fidelity to human performance, whereas the ones on the right measure against an ideal performance measure, called rationality, system is rational if it does the “right thing,” given what it knows.  The Turing Test, proposed by Alan Turing (1950), was designed to provide a satisfactory operational definition of intelligence. A computer passes the test if a human interrogator, after posing some written questions, cannot tell whether the written responses come from a person or from a computer. The interdisciplinary field of cognitive science brings together computer models from AI and experimental techniques from psychology to construct precise and testable theories of the human mind.  An agent is just something that acts (agent comes from the Latin a gere, to do). Of course, all comp...