Week 5 - Reading

Chapter 14

  • Agents almost never have access to the whole truth about their environment.
  • The right thing to do, the rational decision, therefore, depends on both the relative importance of various goals and the likelihood that, and degree to which, they will be achieved.
  • Probability provides a way of summarizing the uncertainty that comes from our laziness and ignorance.
  • Probability theory makes the same ontological commitment as logic, namely, that facts either do or do not hold in the world. Degree of truth, as opposed to degree of belief, is the subject of fuzzy logic.
  • Prior or unconditional probability; after the evidence is obtained, we talk about posterior or conditional probability.
  • An agent is rational if and only if it chooses the action that yields the highest expected utility, averaged over all the possible outcomes of the action
  • If Agent 1 expresses a set of degrees of belief that violate the axioms of probability theory then there is a betting strategy for Agent 2 that guarantees that Agent 1 will lose money.
  • We define the joint probability distribution (or "joint" for short), which completely specifies an agent's probability assignments to all propositions in the domain (both simple and complex).
  • The process of Bayesian updating incorporates evidence one piece at a time, modifying the previously held belief in the unknown variable.

Section 15.4 & 15.5

  • A multiply connected graph is one in which two nodes are connected by more than one path. 
  • There are three basic classes of algorithms for evaluating multiply connected networks, each with its own areas of applicability: 
    • Clustering methods transform the network into a probabilistically equivalent (but topologically different) polytree by merging offending nodes. 
    • Conditioning methods do the transformation by instantiating variables to definite values, and then evaluating a polytree for each possible instantiation. 
    • Stochastic simulation methods use the network to generate a large number of concrete models of the domain that are consistent with the network distribution. They give an approximation of the exact evaluation.
  • logic sampling, we run repeated simulations of the world described by the belief network, and estimate the probability we are interested in by counting the frequencies with which relevant events occur
  • PATHFINDER is a diagnostic expert system for lymph-node diseases, built by members of the Stanford Medical Computer Science program during the 1980s 
  • PATHFINDER I was a rule-based system written with the logical metareasoning system MRS. It did not do any uncertain reasoning.

Section 16.5

  • Decision networks combine belief networks with additional node types for actions and utilities.
  • It illustrates the three types of nodes used:
    • Chance nodes (ovals) 
    • Decision nodes (rectangles) 
    • Utility nodes (diamonds)

Comments

Post a Comment

Popular posts from this blog

Week 7 - Reading

Chapter 20 This kind of feedback is called a reward, or reinforcement. In games like chess, the  reinforcement is received only at the end of the game.  We call this a terminal state in the state history sequence.  The agent can be a passive learner or an active learner. A passive learner simply watches the world going by, and tries to learn the utility of being in various states; an active learner must also act using the learned information, and can use its problem generator to suggest explorations of unknown portions of the environment.  The agent learns an action-value function giving the expected utility of taking a given action in a given state. This is called Q-learning.  We define the reward-to-go of a state as the sum of the rewards from that state until a terminal state is reached. Given this definition, it is easy to see that the expected utility of a state is the expected reward-to-go of that state.  A simple method for updating uti...
Read more