An Object-oriented Multiple Agent Planning System

Kamel, Mohamed S.; Syed, Abbas

doi:10.1016/b978-1-55860-092-8.50016-2

Cited by 13 publications

(4 citation statements)

References 8 publications

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“…One of the important constraints is the operational capabilities of agents. In [7,11], the authors have presented an object oriented muliagent planning approach which is more suitable for agents with similar capabilities. This paper presents a method for generating multiagent plans for agents with disparate capabilities.…”

Section: Related Multiagent Planning Researchmentioning

confidence: 99%

A multiagent task planning method for agents with disparate capabilities

Kamel

Syed

1994

Int J Adv Manuf Technol

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This paper presents a multiagent task planning method for domains where agents (such as machines and robots) have disparate operational capabilities. The method extends the traditional planning for a solitary agent to handle multipleagents through a number of novel concepts and planning approaches. The decomposition of the planning problem into subproblems is aimed at achieving plan execution concurrency, and it is based on the assignment of relations (objects may hold in the domain) to agents. A notion of a substate is used to model the plans and actions of the individual agents. To avoid interagent conflicts, interagent constraints provide the coordination between the concurrent actions of the agents and their interactions. Using the substates and the interagent constraints, a decentralised strategy is realised for generatingand searching the space of alternative feasible subplans of agents. This strategy is illustrated with a search space generated for an example problem. The paper also describes the structure of the system used to implement the approach and the concepts.

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Section: Related Multiagent Planning Researchmentioning

confidence: 99%

A multiagent task planning method for agents with disparate capabilities

Kamel

Syed

1994

Int J Adv Manuf Technol

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“…This process can be realized through cooperation by planning. The combination of problem decomposition and plan generation aspects of multiagent planning have been examined by Kamel and Syed (1989). Central to their approach is the subplanner module, which imposes interagent constraints for interagent interactions.…”

Section: Centralconttollerorconznzoncommunicationptotocol a Central-mentioning

confidence: 99%

Integrated Use of Expert Systems at the K-Tree Level

Chen

AI-Schamma

1991

SIGART Bull.

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“…The synthesis, synchronization, or adjustment process for multiple agent plans thus constitute some of the (varied) foci of DAI planning research. Synchronization through con ict avoidance (George , 1983(George , , 1984Stuart, 1985), distribution of a single-agent planner among multiple agents (Corkill, 1979), the use of a centralized multiagent planner (Rosenschein, 1982), and the use of consensus mechanisms for aggregating subplans produced by multiple agents (Ephrati & Rosenschein, 1993b), have all been explored, as well as related issues (Cohen & Perrault, 1979;Morgenstern, 1987;von Martial, 1992avon Martial, , 1992bKreifelts & von Martial, 1991;Kamel & Syed, 1989;Grosz & Sidner, 1990;Kinny, Ljungberg, Rao, Sonenberg, Tidhar, & Werner, 1992;Ferber & Drogoul, 1992;Kosoresow, 1993).…”

Section: Multiagent Planningmentioning

confidence: 99%

Mechanisms for Automated Negotiation in State Oriented Domains

Zlotkin¹,

Rosenschein

1996

jair

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This paper lays part of the groundwork for a domain theory of negotiation, that is, a way of classifying interactions so that it is clear, given a domain, which negotiation mechanisms and strategies are appropriate. We de ne State Oriented Domains, a general category of interaction. Necessary and su cient conditions for cooperation are outlined. We use the notion of worth in an altered de nition of utility, thus enabling agreements in a wider class of joint-goal reachable situations. An approach is o ered for con ict resolution, and it is shown that even in a con ict situation, partial cooperative steps can be taken by interacting agents (that is, agents in fundamental con ict might still agree to cooperate up to a certain point).A Uni ed Negotiation Protocol (UNP) is developed that can be used in all types of encounters. It is shown that in certain borderline cooperative situations, a partial cooperative agreement (i.e., one that does not achieve all agents' goals) might be preferred by all agents, even though there exists a rational agreement that would achieve all their goals.Finally, we analyze cases where agents have incomplete information on the goals and worth of other agents. First we consider the case where agents' goals are private information, and we analyze what goal declaration strategies the agents might adopt to increase their utility. Then, we consider the situation where the agents' goals (and therefore standalone costs) are common knowledge, but the worth they attach to their goals is private information. We introduce two mechanisms, one \strict," the other \tolerant," and analyze their a ects on the stability and e ciency of negotiation outcomes.De nition 1 A State Oriented Domain (SOD) is a tuple < S; A; J; c > where:1. S is the set of all possible world states; 2. A = fA 1 ; A 2 ; : : :A n g is an ordered list of agents; 3. J is the set of all possible joint (i.e., n-agent) plans. A joint plan J 2 J moves the world from one state in S to another. The actions taken by agent k are called k's role in J, and will be written as J k . We can also write J as (J 1 ; J 2 ; : : :; J n ); 4. c is a function c: J ! (IR + ) n : For each joint plan J in J , c(J) is a vector of n positive real numbers, the cost of each agent's role in the joint plan. c(J) i is the i-th element of the cost vector, i.e., it is the cost of the i-th role in J. If an agent plays no role in J, his cost is 0.

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An Object-oriented Multiple Agent Planning System

Cited by 13 publications

References 8 publications

A multiagent task planning method for agents with disparate capabilities

A multiagent task planning method for agents with disparate capabilities

Integrated Use of Expert Systems at the K-Tree Level

Mechanisms for Automated Negotiation in State Oriented Domains

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