D. Corkill, "Hierarchical planning in distributed environment," in Proc. IJCAI, Cambrige, MA, USA, 1979, pp. 168--179.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....researchers within the area of Distributed Problem Solving (DPS) One common approach is to use a central coordinator. This approach includes Centralized Planning, where one central agent generates the global plan and then hands out pieces of that plan to be performed by the participating agents [48, 9, 67, 69]. Within this centralized approach, some work falls within the category of synchronization of pre existing plans [29, 30] The assumption there is that individual plans are first created, and then submitted to a central planner that is responsible for coordination. In [49] a fast probabilistic ....

D. Corkill. Hierarchical planning in a distributed environment. In Proceedings of the Sixth International Joint Conference on Artificial Intelligence, pages 168--175, Tokyo, August 1979.

....priori agreements, ii) exchange of information about the knowledge each agent has about other agents, iii) exchange of parameters about the state of the agents. d) Distributed Multiagent Planning 4 The model removes the limitation of the previous model of having only a planner. The approach (see [6], 20] 26] works providing each agent with a model of other agents. In a particular application of this field, called Deductive Believe Model (developed by Konolige and Pollack [27] there are two classes of agents: observers and actors. The former class identifies those agents which give ....

D. D. Corkill, "Hierarchical planning in distributed environment", Proceedings of 6th International Joint Conference on Artificial Intelligence, Cambridge, MA, August 1979, p. 168-179

....and [34] Under the general definition of multiagent planning there are several interaction paradigms in which global plans that specify the actions of the agents are built. In this way, there is no conflict among agents. The global plan can be formed in a centralized [24] or in a distributed way [36, 60, 82, 84]. Eaton et al. 50] propose different interaction paradigms as adequate for different situations: assistance, cooperation, and competition. In particular, when an agent (called the user agent) acquires knowledge from another agent (called the provider agent) a cycle of suggestion correction ....

D. D. Corkill. Hierarchical planning in distributed environment. In Proceedings of the Sixth International Joint Conference on Artificial Intelligence, pages 168--179, Cambridge, USA, August 1979.

....helped with the presentation. Our analysis of the limitations of deductive logic builds on previous work by Minsky [15] and the author [8, 9] The ideas in this paper are related to previous work in distributed artificial intelligence. In particular we build on the work of Corkill and Lesser [3, 12]. The approach here differs from Davis and Smith [5] in that organizational mecha nisms are emphasized instead of market mechanisms. This paper describes research done at the Artificial Intelligence Laboratory of MIT. Major support for the research reported in this paper was provided by the ....

CORKILL, D.D. Hierarchical planning in a distributed environment. In Proceedings of the 6th International Joint Con[erence on Artificial Intelligence (Tokyo, Aug.). Kaufman, Los Altos, Calif., 1979, pp. 168-175.

....papers. In coordination, the use of communication is a part of planning and action. It concerns the development of MAS where speech acts are often involved [15, 16, 3, 4, 9] The second aspect comes from the study of interactions which have been of continuing interest in multi agent planning [5, 8, 17, 11, 14, 7]. This mainly focuses on how planning agents can positively cooperate in distributed environments. The last aspect has been studied in [17, 6, 2] The main criticisms to be made about most of the planning models proposed in Multi Agent research are: ffl The formal cooperation models are ....

D.D. Corkill. Hierarchical Planning in a Distributed Environment. In Proc. of the sixth International Joint Conference on Artificial Intelligence (IJCAI-79), 1979.

....uncertainty [ 36 ] For task level cooperation, one common approach is to use a central coordinator (agent) which has a centralized planning. Using this approach, the central agent generates the global plan and then hands out pieces of that plan to be performed by the participating agents [ 5 ] 15 ] 26 ] 27 ] 37 ] Of course there are several methods for relaxing these centralized solution to coordination. One common way is to have some hierarchy of coordinating agents, such that each agent is responsible for the coordination of those who are below it in the hierarchy. ....

....The Dempster Shafer theory is used to describe agents beliefs. In order to describe utilities, we introduce a multi set valued mapping to represent the relationship between action utilities and state partial ignorance. In Section 4, we give the analysis method for the decision criteria. Section 5 is the related works and Section 6 is the summary. 2 Modelling agent beliefs When the agent fuses the information about the environment which comes from other agents or its local sensors, two problems are important. They are: how to describe the PSA of different other agents, and how to ....

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D. Corkill, Hierarchical planning in a distributed environment, in: Proceedings IJCAI-79, Tokyo, 1979, 168-175.

....plan interactions. Table 1 presents an overview of some of the most well known planning approaches in Distributed AI. 13 category domain agent model plan plan interactions main problem addressed STRIPS, 1971; NOAH, 1977 SAP AI planner individual plan creation distributed NOAH, 1979, [4] DP NOAH like AI planner individual plan MA plan parent child relationships extending SAP to DP Konolige Nilsson 1980, 26] CP STRIPS like AI planner individual plan MA plan interacting (sub ) goals modeling MA plans Cammarata et al. 1983, 3] CP air traffic control DAI planner, ....

Corkill, D., Hierarchical Planning in a Distributed Environment, IJCAI-79, pp. 168-175.

....is an action, and it occurs at run time. Each of these assumptions can be contrasted to assumptions that have been made in prior work in Distributed Artificial Intelligence and Multi Agent Systems (George#, 1983; Durfee Lesser, 1987; Genesereth, Ginsberg, Rosenschien, 1986; Tambe, 1997; Corkill, 1979; Wilkens Myers, 1998) Improvement in the performance of the community occurs over several episodes of community activity. During step 2, individuals are satisfied with any solution. In step 3 the actors store into memory the portions of the behavior that they believed were essential to the ....

Corkill, D. D. (1979). Hierarchical planning in a distributed environment. In Proceedings of the Sixth International Joint Conference on Artificial Intelligence, pages 168--175.

.... Distributed Problem Solving also appears in the paradigm of planning for multiple agents, where a single intelligent agent (usually called the master) constructs a plan to be carried out by a group of agents (the slaves) and then hands out the pieces of the plan to the relevant individuals [90, 13, 60]. Werner [109] presents a formal logical model for a master slave relationship by one way communication. Also in the master slave model there is no need to choose a level of effort and there is no need for incentive contracting. That is, the main problem for a master is finding the best plan and ....

D. Corkill. Hierarchical planning in a distributed environment. In Proceedings of the Sixth International Joint Conference on Artificial Intelligence, pages 168--175, Tokyo, August 1979.

....coordinated procedures by MOVERSWORLD actors, is shaped by the fact that communication is the central mechanism for cooperation and coordination. Communication is considered expensive , so it occurs at runtime and is never part of the planning process; this is unlike standard planning systems (Corkill, 1979; Wilkens Myers, 1998) Actors limit themselves to single requests; transmitting single requests lowers both communication costs and plan merging costs, which can be considerable for distributed, independent actors. Two actors are said to cooperate if they act or work together to achieve some ....

Corkill, D. D. (1979). Hierarchical planning in a distributed environment. In Proceedings of the Sixth International Joint Conference on Artificial Intelligence, pages 168--175.

....have been fully elaborated [60] Developing or refining the recipe is a complex activity because of the inter dependencies which exist between the agents actions. Because of this complexity a number of different planning paradigms, each with their own benefits and drawbacks, have been devised [12, 21, 25, 30] for example, the recipe may be generated before any action has started or it may be interleaved with execution, the recipe may be devised by one agent or by a group of agents, and so on. An important facet of the desired cooperation model is that it specifies criteria against which joint ....

D. D. Corkill, Hierarchical Planning in a Distributed Environment, in: Proc. Sixth Int. Joint Conf. on AI, Cambridge, USA (1979) 168-175.

....planning, the reader is advised to see Allen, Hendler and Tate [2] 5.3 Multi agent Planning Multi agent planning in DAI research has evolved along a number of different dimensions. One dimension focuses on agents that cooperate to solve subproblems of a given problem and integrate the results [21,20,30,24,114,36]. Other lines of inquiry concerns planning for task allocation, so that effective execution will result [141,95] centralized planning for multi agent execution [113,67] or centralized planning to avoid execution time action conflicts [50,51] Another area of research has concentrated on multiple ....

D. Corkill. Hierarchical planning in a distributed environment. In Proceedings of the Sixth International Joint Conference on Artificial Intelligence, pages 168--175, Tokyo, August 1979.

....To accomplish this, agents must know how to plan at each of the possible levels of autonomy. Previous and ongoing research provides inroads to these capabilities. The command driven master agent relationship employs traditional centralized planning or distributed centers of planning control [5][6] Planning under the influence of a master agent has also been examined specifically[7] On the other hand, agents developing plans for consensus must negotiate to reach an agreement by which all goals are satisfied. The contract net protocol and its extensions offer agents this capability [8] ....

Corkill, D. 1979. Hierarchical Planning in a Distributed Environment. In Proceedings of the Sixth International Joint Conference on Artificial Intelligence, pp. 168-175.

....(Findler, 1990) Example strategies for Agent Organizational Construction for MAS include Self organization Design (Ishida et al. 1992) Dynamic Adaptive Autonomy (Martin et al. 1996) teamwork models (Tambe, 1997) and so on. Example Plan Generation techniques include hierarchical planning (Corkill, 1979), blackboard systems (Hayes Roth, 1985) and Partial Global Planning (PGP) Durfee and Lesser, 1987) Example techniques developed for Task Allocation include the Contract Net protocols (Smith, 1980) and Group Self Design (Malville and Bourdon, 1998) Plan Integration techniques include Partial ....

Corkill, D. D. 1979. Hierarchical Planning in a Distributed Environment. In Proceedings of the Sixth International Joint Conference on Artificial Intelligence, 168-175.

....(Findler, 1990) Example strategies for Agent Organizational Construction for MAS include Self organization Design (Ishida et al. 1992) Dynamic Adaptive Autonomy (Martin et al. 1996) teamwork models (Tambe, 1997) and so on. Example Plan Generation techniques include hierarchical planning (Corkill, 1979), black board systems (Hayes Roth, 1985) and Partial Global Planning (PGP) Durfee and Lesser, 1987) Example techniques developed for Task Allocation include the Contract Net protocols (Smith, 1980) and the organization approach (Malville and Bourdon, 1998) Plan Integration techniques include ....

Corkill, D. D. 1979. Hierarchical Planning in a Distributed Environment. In Proceedings of the Sixth International Joint Conference on Artificial Intelligence, 168-175.

....actions such as STRIPS (Fikes, 1971) and ADL (Pednault, 1987) Using this codified knowledge and an appropriate reasoning mechanism, agents select actions whose execution will achieve their goals. Many strategies have been developed for the selection of actions, such as hierarchical planning (Corkill, 1979), operator based search methods (Penberthy and Weld, 1992) and a variety of domain specific methods. With multiple agents acting concurrently in the domain, each agent must account for the actions of others while reasoning about selecting actions. For this reason, a representation of the ....

Corkill, D. D. 1979. Hierarchical Planning in a Distributed Environment. Proceedings of the Sixth International Joint Conference on Artificial Intelligence, 168-175.

....channel) The determination of a balance between the acquisition of spatial resource usage information and the overestimation of potential resource conflict areas is an important design issue in distributed planning applications. A more detailed presentation of this research is contained in [10]. The three experiments in distributing the hypothesize and test, iterative refinement, and nonlinear hierarchical planning paradigms discussed in this section indicate that knowledge based AI techniques are potentially useful in FA C distributed systems. However, much research needs to be ....

Daniel D. Corkill. Hierarchical planning in a distributed environment. In Proceedings of the Sixth International Joint Conference on Artificial Intelligence, pages 168--175, Tokyo, Japan, August

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D. Corkill, "Hierarchical planning in distributed environment," in Proc. IJCAI, Cambrige, MA, USA, 1979, pp. 168--179.

No context found.

D. Corkill, "Hierarchical planning in distributed environment," in Proc. IJCAI, Cambrige, MA, USA, 1979, pp. 168--179.

No context found.

Daniel D. Corkill. Hierarchical planning in a distributed environment. In IJCAI-79, 1979.

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Daniel D. Corkill. Hierarchical planning in a distributed environment. In IJCAI-79, 1979.

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D.D. Corkill. Hierarchical planning in a distributed environment. In Proceedings of Sixth IJCAI, August 1979.

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D. D. Corkill. Hierarchical planning in a distributed environment. In Proceedings of the Sixth International Joint Conference on Artificial Intelligence, pages 168--175, Tokyo, August 1979.

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Daniel D. Corkill. Hierarchical planning in a distributed environment. In Proceedings of the Sixth International Joint Conference on Artificial Intelligence, pages 168--175, Tokyo, August 1979.

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Daniel D. Corkill. Hierarchical planning in a distributed environment. In IJCAI-79, 1979.

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