1. Some Thoughts to Consider 5 Take a look at some of the sophisticated toys being offered in stores, in catalogs, or in Sunday newspaper ads. Which ones could be considered ‘intelligent agents’? Which ones have some semblance of AI? One of the reasons for introducing Narl is to demonstrate that knowledge can be represented fairly intuitively, and visually. This is an alternative to the formality of logic languages. Visual languages and metaphors are useful tools for organizing one’s thoughts and knowledge during knowledge acquisition. Some interesting visualization tools that support modeling of all kinds are: http://www.mindjet.com/ http://www.thebrain.com/ http://thesaurus.plumbdesign.com/ http://www.inxight.com/ http://www.touchgraph.com/

2. Where Are We? In the content of chapters 3 through 9, we’ve looked at agents (software systems): That blindly react to environment. That work toward goals by searching for solutions. Involves state-space representation. Involves search strategies. That are ‘knowledge based’. Involves use of logical reasoning. Involves use of a logic representation language. Involves generic schema of Figure 7.1, p. 196. Alternatives for logic language (so far): Propositional logic. First order logic. Important aspects of knowledge based agents: They know about their world (perceived facts). They reason about possible actions. They do this by extensive use of a knowledge base. The knowledge base is constructed from sentences of the logic representation language. The inference mechanism that implements the reasoning is separate from the knowledge base.

3. Heuristic - An Important Concept The word ‘heuristic is derived from a Greek word meaning ‘to find’ or ‘to discover’. Some people use heuristic as the opposite of algorithmic. Newell and Simon: “A process that may solve a given problem, but offers no guarantees of doing so, is called a heuristic for that problem.” Some people view heuristics as ‘rules of thumb’ that domain experts use to generate good solutions without exhaustive search. This view paved the way for rule-based expert systems. Some generalize heuristic to mean ‘knowledge about a problem solution’. Currently, according to Russell and Norvig, heuristic is most often used as an adjective, referring to any technique that improves the average-case performance on a problem- solving task, but does not necessarily improve the worst-case performance.

4. Main Points of Chapter 7 Intelligent agents need knowledge about the world in order to reach good decisions. Knowledge is contained in agents in the form of sentences in a knowledge representation language that are stored in a knowledge base. A knowledge-based agent is composed of a knowledge base and an inference mechanism. It operates by storing sentences about the world in its knowledge base, using the inference mechanism to infer new sentences, and using these sentences to decide what action to take. A representation language is defined by its syntax, which specifies the structure of sentences, and its semantics, which defines the truth of each sentence in each possible world or model. Inference is the process of deriving new sentences from old ones. Propositional logic is a very simple language consisting of proposition symbols and logical connectives. It can handle propositions that are known true, known false, or completely unknown. Inference rules are patterns of sound inference that can be used to find proofs. The resolution rule yields a complete inference algorithm for knowledge bases that are expressed in conjunctive normal form. Forward chaining and backward chaining are very natural reasoning algorithms for knowledge bases in Horn form. Propositional logic is reasonably effective for certain kinds of tasks within an agent, but does not scale to environments of unbounded size because it lacks the expressive power to deal concisely with time, space, and universal patterns of relationships among objects.