Since the early 1970, the AI planning community has been involved in developing artificial planning agent (a predecessor of a deliberative agent), which would be able to choose a proper plan leading to a specified goal.[5] These early attempts resulted in constructing simple planning system called STRIPS. It soon became obvious that STRIPS concept needed further improvement, for it was unable to effectively solve problems of even moderate complexity.[5] In spite of considerable effort to raise the efficiency (for example by implementing hierarchical and non-linear planning), the system remained somewhat weak while working with any time-constrained system.[6]
More successful attempts have been made in late 1980s to design planning agents. For example the IPEM (Integrated Planning, Execution and Monitoring system) had a sophisticated non-linear planner embedded. Further, Wood's AUTODRIVE simulated a behavior of deliberative agents in a traffic and Cohen's PHOENIX system was construed to simulate a forest fire management.[6]
In 1976, Simon and Newell formulated the Physical Symbol System hypothesis,[7] which claims, that both human and artificial intelligence have the same principle - symbol representation and manipulation.[2] According to the hypothesis it follows, that there is no substantial difference between human and machine in intelligence, but just quantitative and structural - machines are much less complex.[7] Such a provocative proposition must have become the object of serious criticism and raised a wide discussion, but the problem itself still remains unsolved in its merit until these days.[6]
Further development of classical symbolic AI proved not to be dependent on final verifying the Physical Symbol System hypothesis at all. In 1988, Bratman, Israel and Pollack introduced Intelligent Resource-bounded Machine Architecture (IRMA), the first system implementing the Belief-Desire-Intention software model (BDI). IRMA exemplifies the standard idea of deliberative agent as it is known today: a software agent embedding the symbolic representation and implementing the BDI.[1]