Rauf

Knowledge-Based Agents

Knowledge-based agents reason by operating on internal representations of knowledge, such as facts and logic.

• If the temperature is low, Rauf avoids a shower.
• Rauf did not shower today.
• Today was particularly cold.

• Rauf didn’t shower today.
• Today was not less cold.

Using facts and logic, knowledge-based agents can infer new knowledge, as demonstrated by the above example.

Propositional Logic

Propositional logic involves statements that can either be true or false, forming the foundation for logical reasoning.

if today_is_cold and no_shower_taken:
    print("Rauf avoided the shower.")
      

Entailment

Entailment in logic means that if certain facts are true, they logically imply other facts.

if "Today is cold" entails "Rauf avoids the shower":
    print("Entailment holds true.")
      

Inference

Inference is the process of deriving logical conclusions from given facts or premises.

facts = ["Today is cold", "Rauf avoids shower"]
if "Rauf avoided the shower" in facts:
    print("Inference successful.")
      

Model Checking

Model checking is verifying if a logical statement holds true under a given set of conditions.

conditions = {"cold": True, "shower": False}
if conditions["cold"] and not conditions["shower"]:
    print("Model satisfies conditions.")
      

Resolution

Resolution is a rule of inference used for automated reasoning and proof.

if "cold" and "not shower":
    print("Resolution proves Rauf avoided the shower.")
      

First Order Logic

First-order logic extends propositional logic by including quantifiers like "for all" or "there exists."

forall x (Cold(x) -> AvoidShower(x))
if Cold("today"):
    print("Rauf avoids the shower.")