Acting Rationally - artificial intelligence, Basic Computer Science

Acting Rationally:

Al Capone was finally convicted for tax evasion. Were the police acting rationally? To answer this, we must first look at how the performance of police forces is

Viewed: convicting and arresting the people who have committed a crime is a began, but  their  success  in  stop criminal on  the  street  is  also  a  reasonable,  if measure ,contentious. Given that they didn't prisoner Capone for the murders he committed, they failed on that scale. Although, they did get him off the street, so they succeeded there. We might also look at the what the police knew and what they had experienced about the environment: they had experienced murders which they knew were undertaken by Capone,  but  they had not  experienced any evidence which could convict Capone of the murders. However, they had evidence of tax evasion. Given the knowledge about the environment that they can only arrest if they have evidence, their actions were therefore limited to arresting Capone on tax evasion. As this got him off the street, we could say they were acting rationally.

This answer is controversial, and highlights the reason why we have to think hard about how to assess the rationality of an agent before we consider building it.

To summarize, an agent takes input from its environment and affects that environment. The rational performance of an agent must be assessed in terms of the task it was meant to undertake, it's knowledge and experience of the environment and  the actions  it  was actually able to  undertake.  This performance should  be objectively measured independently of any internal measures used by the agent.

In English language usage, autonomy means an ability to govern one's actions independently. In our situation, we need to specify the extent to which an agent's behavior is affected by its environment. We say that:

  • The autonomy of an agent is measured by the extent to which its behaviour is determined by its own experience.

At  one extreme, an agent  might  never pay any attention to the input  from its environment,  in  which  case,  its  actions  are  determined  entirely  by  its  built -in knowledge. At the other extreme, if an agent does not initially act using its built-in knowledge,  it  will  have  to  act  randomly,  which  is  not  desirable.  Hence,  it  is desirable  to  have  a  balance  between  complete  autonomy  and  no  autonomy. Thinking of human agents, we are born with certain reflexes which govern our actions to begin with. However, through our ability to learn from our environment, we begin to act more autonomously as a result of our experiences in the world. Imagine a baby learning to crawl around. It must use in-built information to enable it to correctly employ its arms and legs, otherwise it would just thrash around. However, as it  moves, and bumps into things, it  learns to avoid objects in the environment. When we leave home, we are (supposed to be) fully autonomous agents ourselves. We should expect similar of the agents we build for AI tasks: their autonomy increases in line with their experience of the environment.

Posted Date: 10/1/2012 8:59:58 AM | Location : United States







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