Evaluating Rationality and Agent Behavior in Intelligent Systems

This assignment evaluates the concepts of rationality and agents within intelligent systems, addressing several key statements from Russell and Norvig's "Artificial Intelligence: A Modern Approach." It clarifies that an agent with partial information can still be rational, depending on its actions based on available percepts. The assignment confirms that pure reflex agents may not behave rationally in all task environments due to their lack of memory of past percepts. It also explains that environments exist where all agents are rational, such as those with uniform rewards. Finally, the input to an agent program is affirmed to be the current percept, aligning with the input to the agent function, albeit with the agent function considering the entire percept history. Desklib provides access to a wealth of solved assignments and study materials for students.

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Intelligent Systems
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An agent that senses only partial information about the state cannot be perfectly rational.
(Page 31)
This statement is false. It would be unwise to judge an agent on not acting rational for only
having partial data since it could only act on what it perceives and thus acting rational as much as
much as it possibly can for having some variables which is not accounted for by no fault of its
own (Russell and Norvig, 2016). For each of the potential percept series, the rational agent
should choose an activity that is anticipated to increase its performance measure, presented
evidence which is provided by percept sequence and no matter what built-in information the
agent has (Russell and Norvig, 2016).
There exist task environments in which no pure reflex agent can behave rationally. (Pages
40-41)
This statement is true. Pure reflex agent generally ignores on the earlier percepts, hence, could
not obtain an optimal state estimation in a particular partial perceptible environment (Russell and
Norvig, 2016). For instance, correspondence chess is presented out by providing particular
moves; in the event the other players change is present percept, a reflex agent might not maintain
some track of the board state and may have to respond to, for instance a4, in the means
regardless of the position that has been played (Russell and Norvig, 2016).
There exists a task environment in which every agent is rational. (Pages 32-33)
This statement is true. When it comes to an environment that has a single state, for example all
the actions which have the same reward, it might not matter which action are taken (Russell and
Norvig, 2016). Moreover, any environment that is incentive invariant under the permutation to
the actions would certainly satisfy this property (Russell and Norvig, 2016). It is important to

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note that the only was this scenario could possibly take place is if there was an environment that
all the moves were set to offer the same output (Russell and Norvig, 2016). Agents that are
rational look to maintain efficiency thus, if all the moves equaled to the exact reward then this
assertion might be true (Russell and Norvig, 2016).
The input to an agent program is the same as the input to the agent function. (page 35-36).
This statement is true. When it comes to the gent functionality, notionally speaking, takes as
input the whole percept sequences as much as that primarily level, whereas the agent program
takes the existing percept solely (Russell and Norvig, 2016). It could also be noted that the agent
input as well as agent function have both to do with precepts (Russell and Norvig, 2016).
However, there are major differences top the two agent program which is the current precept
while the agent function is regarded as the precept history (Russell and Norvig, 2016).

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References
Russell, S.J. and Norvig, P., 2016. Artificial intelligence: a modern approach. Malaysia; Pearson
Education Limited,.