In this paper we describe some major new additions to the STRIPS robot problem-solving Before getting into details (and defining just what we mean by generalize), system. The first addition is a process for generalizing a plan produced by STRIPS so that problem-specific constants appearing in the plan are replaced by problem-independent parameters.

A plan for a question required the composition of REQUEST and INFORM and led to the development of two new kinds of informing speech acts, INFORMREF To plan a yes/no question about some proposition P. one should think that the and INFORMIF, and their mediating acts. The INFORMREF acts lead to hearer knows whether P is true or false (or, at least "might know"). An approximate "what," "when," and "where" questions while INFORMIF results in a yes/no representation of AGT2's knowing whether P is true or false is OR (AGT2 question.2' The reason for these new acts is that, in planning a REQUEST that BELIEVE P, AGT2 BELIEVE -- P)).'9 Such goals are often created, as modelled someone else perform an INFORM act, one only has incomplete knowledge of by our type 4 inference, when a planner does not know the truth-value of P. their beliefs and goals; but an INFORM, as originally defined can only be Typical circumstances in which an agent may acquire such disjunctive beliefs planned when one knows what is to be said.

In order to generate plans for agents with multiple actuators, agent teams, or distributed controllers, we must be able to represent and plan using concurrent actions with interacting effects. This has historically been considered a challenging task requiring a temporal planner with the ability to reason explicitly about time. We show that with simple modifications, the STRIPS action representation language can be used to represent interacting actions. Moreover, algorithms for partial-order planning require only small modifications in order to be applied in such multiagent domains. We demonstrate this fact by developing a sound and complete partial-order planner for planning with concurrent interacting actions, POMP, that extends existing partial-order planners in a straightforward way. These results open the way to the use of partial-order planners for the centralized control of cooperative multiagent systems.

The Sphinx once challenged a particularly tasty-looking student of language to solve the riddle: "How is saying'My toc is turning blue,' as a request to get off my toe, similar to slamming a door in someone's face?" The poor student stammered that in both cases, when the agents are trying to communicate something, they have analogous intentions. "Yes indeed" countered the Sphinx, "but what are those intentions?" Hearing no reply, the monster promptly devoured the poor student and sat back smugly to wait for the next oral exam. The research described herein was supported primarily by the National Research Council of Canada, and also by the National Institute of Education under Contract US-N1E-C-400-76-0116, the Department of Computer Science of the University of Tor3nto, and by a summer graduate student associateship (1975) to Cohen from the International Business Machines Corporation.

"In this paper we describe some major new additions to the STRIPS robot problem-solving system. The first addition is a process for generalizing a plan produced by STRIPS so that problem-specific constants appearing in the plan are replaced by problem-independent parameters.The generalized plan, stored in a convenient format called a triangle table, has two important functions. The more obvious function is as a single macro action that can be used by STRIPS—either in whole or in part—during the solution of a subsequent problem. Perhaps less obviously, the generalized plan also plays a central part in the process that monitors the real-world execution of a plan, and allows the robot to react "intelligently" to unexpected consequences of actions.We conclude with a discussion of experiments with the system on several example problems."Artificial Intelligence 3:251-288