• Unit 10: Reasoning Agents

    Reasoning is one of the important core capabilities mentioned in the Turing test for intelligence. Reasoning allows an agent to infer or deduce new information from information already known to be true. Reasoning occurs in different "logics": languages of varying power and other constructs to describe the known characteristics, which include Boolean or propositional logic and first-order logic (FOL). FOL is better capable of compactly describing known information about a system. This unit will help you understand these logics and how inference is carried out in propositional logic and FOL. Finally, we will look at uncertainty models such as Bayesian analysis, Bayesian networks, and Markov Chains, which have proved better than logic-based systems in modeling uncertainty and predicting probabilistic outcomes.

    Completing this unit should take you approximately 8 hours.

    • Upon successful completion of this unit, you will be able to:

      • explain the foundations of propositional logic;
      • describe the methods of reasoning algorithms such as modus ponens and resolution over propositional logic;
      • describe the foundations of first-order logic;
      • describe the methods of reasoning algorithms such as modus ponens and resolution over first-order logic;
      • describe the foundations of reasoning under uncertainty and its importance to intelligent agents;
      • apply Bayesian analysis to predict probabilistic outcomes in problems that have uncertainty;
      • apply Bayesian networks to model probabilistic relationships between discrete variables; and
      • describe how Markov chains and hidden Markov chains relate to uncertainty reasoning.

    • 10.1: Propositional Logic

      Propositional or Boolean logic is a simple (but limited) notation to describe the knowledge associated with different problem domains. Because the notation is limited, describing complex systems using propositional logic can take time and effort. As we review, we will refer to principles such as modus ponens (forward and backward chaining) and resolution over propositional logic.

    • 10.2: First Order Logic

      First-order logic is a powerful (but fairly complex) notation to describe the knowledge associated with different problem domains. FOL is a far better notation than propositional logic to describe systems, but it can be more challenging to follow or understand. Using first-order logic, we will review inference principles such as modus ponens (forward and backward chaining) and resolution.

    • 10.3: Bayesian Reasoning and Uncertainty

      An inherent part of intelligence is being able to handle uncertainty effectively. Specifically, we will discuss the framework of conditional probability and use Bayes' theorem as the foundation to model the influence of variables on outcomes. Using Bayes' rule, we can probabilistically predict the strategy to use.

    • 10.4: Modeling Causality with Bayesian Networks

      A Bayesian network can model causal relationships probabilistically. Given certain evidence, Bayesian analysis can probabilistically predict the explanation for the evidence. Markov chains and hidden Markov chains are formal ways to model uncertainty in dynamic systems that change state in specific ways.