M. Georgeff, A. Lansky, and M. Schoppers. Reasoning and planning in dynamic domains: An experiment with a mobile robot. Technical Report 380, A.I. Center, SRI International, 1986.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....on real world applications where failure is a key aspect. This is the case of the research on embedded planners . Several embedded planners have been proposed so far (see for instance [7, 13, 2, 4, 15] and have been successfully applied in particular application domains (like mobile robots [6, 14, 3] and fault diagnosis for real time systems [7] Most of them provide different and flexible failure handling mechanisms, see for instance [6, 13] Nevertheless, so far this research has focused on domain specific applications and on system architectures. As a consequence, the current work is far ....

.... been proposed so far (see for instance [7, 13, 2, 4, 15] and have been successfully applied in particular application domains (like mobile robots [6, 14, 3] and fault diagnosis for real time systems [7] Most of them provide different and flexible failure handling mechanisms, see for instance [6, 13]. Nevertheless, so far this research has focused on domain specific applications and on system architectures. As a consequence, the current work is far for providing a general theoretical framework for representing and reasoning about failure. A partial exception is the work described in 11 [7] ....

M. Georgeff, A. Lansky, and M. Schoppers. Reasoning and planning in dynamic domains: An experiment with a mobile robot. Technical Report 380, A.I. Center, SRI International, 1986.

.... in the background, applying this rule when the method is instantiated will add a task to the expansion that won t interfere with other steps in the method and might save some time later by noticing a trash can that would otherwise have been missed. The system supports such rules as meta KAs [6]. Similar rules can be used to address some of the problems with overlapping tasks discussed in the previous section. For example, one might want a rule that says: Whenever a action is taken the expansion of , a task should be created. This could be combined with a rule that says: Whenever an ....

Michael P. Georgeff, Amy L. Lansky, and Marcel J. Schoppers. Reasoning and planning in dynamic domains: An experiment with a mobile robot. Tech Note 380, AI Center, SRI International, 1986.

....i.e. a set of task sequences and contingencies which lead the system to its goal (or to failure) The search through policies to achieve system goals is done by the programmer at the time of writing the program. Strategic reasoning systems, such as the Procedural Reasoning System (PRS) M. P. Georgeff, 1987] or the Reactive Action Packages (RAP) system [Firby, 1989] provide flexible, robust performance, especially when there are multiple methods available to attain a goal. They allow extremely flexible and compact specification of contingency behaviors, and have the possibility of responding to ....

M. P. Georgeff, A. L. Lansky, M. J. S. (1987). Reasoning and planning in dynamic domains: An experiment with a mobile robot. Technical Report 380, SRI.

....unavoidable) shortcomings. The planning ahead approach seems not suited to application domains in which highly unpredictable events may occur, while the reactive based approach is unable to reproduce high level behaviors. Efforts have been spent in trying to integrate the two approaches [6, 7], but no attempt has been made towards the design of a general 2 framework where the two approaches can be flexibly integrated. Given the functionalities of MRG, and high unpredictability of its application environment, both the reactive and the planning ahead paradigms turn out to be ....

M. Georgeff, A. Lansky, and M. Schoppers. Reasoning and planning in dynamic domains: An experiment with a mobile robot. Technical Report 380, A.I. Center, SRI International, 1986.

....systems [Bro86, Kae87] base their behavior almost completely on the information acquired from the external environment, although a limited planning ahead phase seems useful in order to build a system showing high level behaviours. Therefore navigation systems are becoming more and more integrated [GLS86, Sim91], able to plan ahead and to acquire information from the external environment; their behaviour is based both on This work has been done as part of the project MAIA , Advanced Model of Artificial Intelligence, under development at IRST. The research described here is partly supported by the ....

M. Georgeff, A. Lansky, and M. Schoppers. Reasoning and planning in dynamic domains: An experiment with a mobile robot, 1986. SRI Technical Note 380.

....perform different failure recovering mechanisms depending on the different situations of the external environment. Several embedded planners have been proposed so far [BM92, Fir92, GL86, GTCS91, Sim90, SCT92] and have been successfully applied in particular application domains (like mobile robots [CTDA92, Gat92, GLS86, Sim91] and fault diagnosis for real time systems [GL86] Nevertheless, so far most of the work on embedded planning has concentrated in domain specific applications and in system design and architectures. As a consequence, current architectures are far for providing a general framework where all the ....

M. Georgeff, A. Lansky, and M. Schoppers. Reasoning and planning in dynamic domains: An experiment with a mobile robot. Technical Report 380, A.I. Center, SRI International, 1986.

....reasoning of a robot that combines traditional means end reasoning with the abilities to react to unanticipated events and to change goals and intentions as situations warrant. PRS has been used to control the movements of a real robot operating in an singleagent, indoor navigation domain [GLS87] A PRS agent comprises a set of changing beliefs (facts about the world) a set of current goals or desires, a set of procedural plan schemas or Knowledge Areas (KAs) which describe how to achieve its goals and how to react to particular events, an interpreter for manipulating each of these ....

....and algorithms to perform these functions in a number of different layers. As Hanks and Firby [HF90, page 67 68] argue, the division between uniform and layered approaches reflects a bias as to which problems the architectures implementors wish to address. Uniform architectures such as PRS [GLS87, GI89] or GUARDIAN [HR88, HR90] for example, make the assumption that action and deliberation are so closely related that these cannot usefully be handled separately. 9 By design, uniform architectures can easily bring all of their deliberation machinery to bear on every individual action ....

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Michael P. Georgeff, Amy L. Lansky, and Marcel J. Schoppers. Reasoning and planning in dynamic domains: An experiment with a mobile robot. Technical Note 380, SRI International, Menlo Park, CA, April 1987.

....internal representation or explicit reasoning systems [Brooks, 1991] This architecture is based on reactive agents that must respond dynamically to changes in their environment. The hybrid architecture attempts to harmonize the classical architecture with the reactive approach [Arkin, 1990] [Georgeff, Lansky and Schoppers, 1987]. The present work proposes a hybrid architecture for autonomous agents in a virtual environment. However, the emphasis of this work is on the reactive side of the hybrid architecture. The authors believe that a special agent language for behavioral animation should be developed. However, this is ....

....learned procedure MoveTo. Learned procedures are not based on traditional AI techniques. They are compiled programs that are very efficient. Also there is no need for explanation based reasoning. Learned procedures implement the reactive behavior of the characters in the same spirit proposed by [Georgeff, Lansky and Schoppers, 1987] and [Brooks, 1991] The agents are driven by their learned procedures and the intelligence they exhibit is a result of the interactions that occur within the virtual environment. The principles of emergence and situatedness are satisfied by the learned procedures. Behavior functions are ....

M. P. Georgeff, A. L. Lansky, M. J. Schoppers, Reasoning and Planning in Dynamic Domains: An Experiment with a Mobile Robot, Technical Report 380, Artificial Intelligence Center, SRI International, Menlo Park, CA, 1987.

....and for dealing with the objects it encountered along the way. Such a system would have a good idea of when it was done because it could tell by its map and its object classifications. A variety of architectures for managing plans dynamically would be appropriate for such a system: RAPs [4] PRS [6], and Universal Plans [11] to name just a few. Situations and plans are the natural vocabulary for discussing solutions to the synthetic vacuum task because we, as humans, seem to conceptualize the problem in those terms. We can recognize and classify objects and situations with apparent ease and ....

Michael P. Georgeff, Amy L. Lansky, and Marcel J. Schoppers. Reasoning and planning in dynamic domains: An experiment with a mobile robot. Tech Note 380, AI Center, SRI International, 1986.

....and propagate them to its children. This notion would improve the control mechanism because it represents more accurately the status of the robot execution tasks. 5 Related Work The earliest work on using PRS to control mobile robot is a study performed by Georgeff et al. at SRI and described in [6]. One of the major criticisms one can make to this study is that it never reached a point where a real robot ran under the control of SRI PRS. For various reasons, but mainly performances, the procedures were ran with a robot simulator. Moreover, the version of SRI PRS used at that time lacked ....

M. P. Georgeff, A. L. Lansky, and M. Schoppers. Reasoning and planning in dynamic domains: an experiment with a mobile robot. TN380, AI Center, SRI International, Menlo Park, California (USA), 1987.

....activity continues with the first step of the loop. 5. Discussion Several systems have been created in the past few years with the intent of combing reactivity with rich goal directed behaviors. Reactive Action Packages (RAPs) by Firby [9] the Procedural Reasoning System (PRS) by Georgeff, et al. [10, 11], and the Task Control Architecture (TCA) by Simmons [13, 15] are all particularly similar to Hap in that they provide an explicit representation of goals, provide mechanisms to respond quickly to emergencies, and provide the capability to handle multiple active goals. Hap differs from these ....

Michael P. Georgeff, Amy L. Lansky, and Marcel J. Schoppers. Reasoning and planning in dynamic domains: An experiment with a mobile robot. Technical Report Technical Note 380, Artificial Intelligence Center, SRI International, Menlo Park, CA, 1987.

....build broad, believable agents, we needed a suitable language. In our case, we use the Hap language of Loyall and Bates [Loyall96] The Hap language has been designed specifically to support believable agents, although it is similar to other work such as reactive architectures (e.g. Firby89] and [Georgeff87]) behavior based architectures (e.g. Brooks86] and situated action (e.g. Agre90] and [Suchman88] Throughout the thesis, I will point out various ways that I have extended the basic Hap language to better suit the task of building social and emotional agents. It is not necessary to ....

Georgeff, M. and Lansky, A. and Schoppers, M. Reasoning and Planning in Dynamic Domains: An Experiment with a Mobile Robot. Technical Report 380, Artificial Intelligence Center, SRI International. Menlo Park, CA. BELIEVABLE SOCIAL AND EMOTIONAL AGENTS 283 1987.

....inability to fully model the agent rich world they inhabit. Thus, we suspect that some of our experience with broad agents in Oz may transfer to the domain of social, real world robots [5] Building broad agents is a little studied area. Much work has been done on building reactive systems [1, 6, 7, 10, 11, 23], natural language systems (which we do not discuss here) and even emotion systems [9, 19, 21] There has been growing interest in integrating action and learning (see [16] and some very interesting work on broader integration [24, 20] However, we are aware of no other efforts to integrate the ....

Michael P. Georgeff, Amy L. Lansky, and Marcel J. Schoppers. Reasoning and planning in dynamic domains: An experiment with a mobile robot. Technical Report 380, Artificial Intelligence Center, SRI International, Menlo Park, CA, 1987.

....a general inability to fully model the agent rich world they inhabit. Thus, we suspect that some of our experience with broad agents in Oz may transfer to the domain of social, real world robots. Building broad agents is a little studied area. Much work has been done on building reactive systems [1, 6, 7, 10, 11, 22], natural language systems (which we do not discuss here) and even emotion systems [9, 18, 20] There has been growing interest in integrating action and learning (see [16] and some very interesting work on broader integration [23, 19] However, we are aware of no other efforts to integrate the ....

Michael P. Georgeff, Amy L. Lansky, and Marcel J. Schoppers. Reasoning and planning in dynamic domains: An experiment with a mobile robot. Technical Report 380, Artificial Intelligence Center, SRI International, Menlo Park, CA, 1987.

....to a deep level of competence will not be as important as creating a broad set of integrated capabilities. Loyall and Bates developed the Hap language [6] as a foundation for broad agents. Hap is a behavior based language in the spirit of Firby s RAP system [4] and Georgeff and Lansky s PRS system [5]. Like other behaviorbased languages, Hap makes it natural to build behaviors that are robust and reactive. Unlike some other behavior based frameworks, however, Hap uses explicit goals to organize behavior, which we feel helps give our agents the appearance of intention. We have previously built ....

Michael P. Georgeff, Amy L. Lansky, and Marcel J. Schoppers. Reasoning and planning in dynamic domains: An experiment with a mobile robot. Technical Report 380, Artificial Intelligence Center, SRI International, Menlo Park, CA, 1987.

....general inability to fully model the agent rich world they inhabit. We suspect that some of our experience with broad agents in Oz may transfer to other domains, such as social, real world robots. Building broad agents is a little studied area. Much work has been done on building reactive systems [6, 10, 9, 24], natural language systems, and even emotion systems [8, 23, 21] There is growing interest in integrating action and learning (see [14] and some very interesting work on broader integration [25, 22] However, we are aware of no other efforts to integrate the particularly wide range of ....

Michael P. Georgeff, Amy L. Lansky, and Marcel J. Schoppers. Reasoning and planning in dynamic domains: An experiment with a mobile robot. Technical Report 380, Artificial Intelligence Center, SRI International, Menlo Park, CA, 1987.

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M. Georgeff, A. Lansky and M. Schoppers. Reasoning and planning in dynamic domains: an experiment with a mobile robot. Tech Note 380, AI Center, SRI International, 1986.

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George#, M. P.; Lansky, A. L.; and Schoppers, M. J. 1987. Reasoning and planning in dynamic domains: An experiment with a mobile robot. Technical Report 380, Artificial Intelligence Center, SRI International.

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M. Georgeff, A. Lansky, and M. Schoppers, "Reasoning and planning in dynamic domains: an experiment with a mobile robot", SRI Tech Note 380, (1986).

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Georgeff, M.P., A.L. Lansky, and M. Schoppers, "Reasoning and Planning in Dynamic Domains: An Experiment With a Mobile Robot," SRI Artificial Intelligents Center Technical Note 380, Artificial Intelligents Center, SRI International, Menlo Park, California, 1987.

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Georgeff, M. P.; Lansky, A. L.; and Schoppers, M. J. 1987. Reasoning and planning in dynamic domains: An experiment with a mobile robot. Technical Report 380, Artificial Intelligence Center, SRI International.

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Georgeff, M. P.; Lansky, A. L.; and Schoppers, M. J. 1987. Reasoning and planning in dynamic domains: An experiment with a mobile robot. Technical Report 380, Artificial Intelligence Center, SRI International.

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