Two forms of cooperation in distributed problem solving are considered: task-sharing and result-sharing, and the basic methodology is presented and systems in which it has been used are described.
Abstract:
Two forms of cooperation in distributed problem solving are considered: task-sharing and result-sharing. In the former, nodes assist each other by sharing the computational load for the execution of subtasks of the overall problem. In the latter, nodes assist each other by sharing partial results which are based on somewhat different perspectives on the overall problem. Different perspectives arise because the nodes use different knowledge sources (KS's) (e.g., syntax versus acoustics in the case of a speech-understanding system) or different data (e.g., data that is sensed at different locations in the case of a distributed sensing system). Particular attention is given to control and to internode communication for the two forms of cooperation. For each, the basic methodology is presented and systems in which it has been used are described. The two forms are then compared and the types of applications for which they are suitable are considered.
TL;DR: A generic ASM ground model is derived here as a framework for defining the precise meaning of fundamental situation analysis concepts applicable to different application domain models.
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TL;DR: Comparisons with PLANNER, CONNIVER, HEARSAY-11, and PUP6 are used to demonstrate that negotiation - the two-way transfer of information - is a natural extension to the transfer of control mechanisms used in earlier problem-solving systems.
TL;DR: In this article, the contract net protocol has been developed to specify problem-solving communication and control for nodes in a distributed problem solver, where task distribution is affected by a negotiation process, a discussion carried on between nodes with tasks to be executed and nodes that may be able to execute those tasks.
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TL;DR: A framework called the contract net is presented that specifies communication and control in a distributed problem solver, and comparisons with planner, conniver, hearsay-ii, and pup 6 are used to demonstrate that negotiation is a natural extension to the transfer of control mechanisms used in earlier problem-solving systems.
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Q1. What have the authors contributed in "Frameworks for cooperation in distributed problem solving" ?
For each, the basic methodology is presented and systems in which it has been used are described.
Q2. What is the purpose of the answer synthesis?
Answer synthesis is performed in the third phase; that is, integration of subproblem results to achieve a solution to the overall problem.
Q3. How do the authors use negotiation to solve the connection problem?
In order to maximize system concurrency, both nodes with tasks to be executed and nodes ready to execute tasks can proceed simultaneously, engaging each other in a process that resembles contract negotiation to solve the connection problem.
Q4. How many bits must be communicated by each node?
Assume that each of the nodes operates at 108 instructions per second; the computation and communication load is shared equally by all nodes, and the problem-solving architecture is such that one bit must be communicated by each node for every ten instructions that it executes.
Q5. What is the common metaphor for a problem solver?
A familiar metaphor for a problem solver operating in a distributed processor is a group of human experts experienced at working together, trying to complete a large task.
Q6. What is the purpose of the contract net protocol?
The connection that is effected with the contract net protocol is an extension to the pattern-directed invocation used in many AI programming languages (see [5] for anin-depth discussion).
Q7. What is the definition of a block world image?
A blocks world image is a line drawing that shows the edges of a collection of simple objects (e.g., cubes, wedges, and pyramids) in a scene.
Q8. What is the key to achieving consistent image labeling?
Thus the key to achieving consistent image labeling is to compare the label set of each vertex with those of its neighbors and discard inconsistent labels.