Diagnosis of plan failures is an important subject in both single- and multi-agent planning. Plan... more Diagnosis of plan failures is an important subject in both single- and multi-agent planning. Plan diagnosis can be used to deal with plan failures in three ways: (i) it provides information necessary for the adjustment of the current plan or for the development of a new plan, (ii) it can be used to point out which equipment and/or agents should be repaired or adjusted so they will not further harm the plan execution, and (iii) it can identify the agents responsible for plan-execution failures. We introduce two general types of plan diagnosis: primary plan diagnosis identifying the incorrect or failed execution of actions, and secondary plan diagnosis that identifies the underlying causes of the faulty actions. Furthermore, three special cases of secondary plan diagnosis are distinguished, namely agent diagnosis, equipment diagnosis and environment diagnosis.
Abstract We introduce an algorithm for cooperative planning in multi-agent systems. The algorithm... more Abstract We introduce an algorithm for cooperative planning in multi-agent systems. The algorithm enables the agents to combine (fuse) their plans in order to increase their joint profits. A computational resources and skills framework is developed for representing the planned activities of an agent under time constraints. Using this resource-skill framework, we present an efficient (polynomial time) algorithm that fuses the plans of a group of agents in such a way that their joint profits improve. The framework and the algorithm are illustrated ...
We consider coordination problems where several agents, each assigned to some subtask of a comple... more We consider coordination problems where several agents, each assigned to some subtask of a complex task, solve their own sub-task by making minimal plans and want to find a common plan based on their individual plans. A task is conceived as a set of primitive tasks (operations), partially ordered by a set of precedence constraints. Operations are distributed among agents dependent on their capabilities and constitute the subtasks the agents have to solve. The precedence constraints between operations in subtasks are inherited from the overall precedence constraints occurring in the task. Since it is assumed that every agent is capable to find a suitable (minimal) plan for its own sub-task, the main problem for the agents to coordinate their plans in order to solve the complete task. First, we characterize situations in which an optimal coordinated plan can be constructed by simple plan coordination. Since, in general, obtaining optimal global plans is intractable, we therefore introduce two simple and efficient distributed approximation algorithms to achieve plan coordination.The first algorithm can be used as a d-approximation of a globally optimal plan for the agents, where d is the depth of the original task, i.e. the length of the longest chain in the set of precedence constraints constituting the task. This algorithm assumes almost no knowledge about the distribution of tasks over the agents. If such knowledge, however, is available, a second, more refined algorithm can be used, that is based on elaborate inter-agent negotiation and is able to achieve a better approximation ratio.
Abstract Many day-to-day situations involve decision making: for example, a taxi company has some... more Abstract Many day-to-day situations involve decision making: for example, a taxi company has some transportation tasks to be carried out, a large firm has to distribute a lot of complicated tasks among its subdivisions or subcontractors, and an air-traffic controller has to assign time slots to planes that are landing or taking off. Intelligent agents can aid in
We consider a model-based diagnosis approach to the diagnosis of plans. Here, a plan performed by... more We consider a model-based diagnosis approach to the diagnosis of plans. Here, a plan performed by some agent(s) is considered as a system to be diagnosed. We introduce a simple formal model of plans and plan execution where it is assumed that the execution of a plan can be monitored by making partial observations of plan states. These observed states are used to compare them with states predicted based on (normal) plan execution. Deviations between observed and predicted states can be explained by qualifying some plan steps in the plan as behaving abnormally. A diagnosis is a subset of plan steps qualified as abnormal that can be used to restore the compatibility between the predicted and the observed partial state. Besides minimum and subset minimal diagnoses, we argue that in plan-based diagnosis maximum informative diagnoses should be considered as preferred diagnoses, too. The latter ones are diagnoses that make the strongest predictions with respect to partial states to be observed in the future. We show that in contrast to minimum diagnoses, finding a (subset minimal) maximum informative diagnosis can be achieved in polynomial time. Finally, we show how these diagnoses can be found efficiently if the plan is distributed over a number of agents.
We consider planning problems where a number of non-cooperative agents have to work on a joint pr... more We consider planning problems where a number of non-cooperative agents have to work on a joint problem. Such problems consist in completing a set of interdependent, hierarchically ordered tasks. Each agent is assigned a subset of tasks to perform for which it has to construct a plan. Since the agents are non-cooperative, they insist on planning independently and do not want to revise their individual plans when the joint plan has to be assembled from the individual plans. We present a general formal framework to study some computational aspects of this non-cooperative coordination problem and we establish some complexity results to identify some of the factors that contribute to the complexity of this problem. Finally, we illustrate our approach with an application to coordination in multi-modal logistic planning.
Diagnosis of plan failures is an important subject in both single- and multi-agent planning. Plan... more Diagnosis of plan failures is an important subject in both single- and multi-agent planning. Plan diagnosis can be used to deal with plan failures in three ways: (i) it provides information necessary for the adjustment of the current plan or for the development of a new plan, (ii) it can be used to point out which equipment and/or agents should be repaired or adjusted so they will not further harm the plan execution, and (iii) it can identify the agents responsible for plan-execution failures. We introduce two general types of plan diagnosis: primary plan diagnosis identifying the incorrect or failed execution of actions, and secondary plan diagnosis that identifies the underlying causes of the faulty actions. Furthermore, three special cases of secondary plan diagnosis are distinguished, namely agent diagnosis, equipment diagnosis and environment diagnosis.
Abstract We introduce an algorithm for cooperative planning in multi-agent systems. The algorithm... more Abstract We introduce an algorithm for cooperative planning in multi-agent systems. The algorithm enables the agents to combine (fuse) their plans in order to increase their joint profits. A computational resources and skills framework is developed for representing the planned activities of an agent under time constraints. Using this resource-skill framework, we present an efficient (polynomial time) algorithm that fuses the plans of a group of agents in such a way that their joint profits improve. The framework and the algorithm are illustrated ...
We consider coordination problems where several agents, each assigned to some subtask of a comple... more We consider coordination problems where several agents, each assigned to some subtask of a complex task, solve their own sub-task by making minimal plans and want to find a common plan based on their individual plans. A task is conceived as a set of primitive tasks (operations), partially ordered by a set of precedence constraints. Operations are distributed among agents dependent on their capabilities and constitute the subtasks the agents have to solve. The precedence constraints between operations in subtasks are inherited from the overall precedence constraints occurring in the task. Since it is assumed that every agent is capable to find a suitable (minimal) plan for its own sub-task, the main problem for the agents to coordinate their plans in order to solve the complete task. First, we characterize situations in which an optimal coordinated plan can be constructed by simple plan coordination. Since, in general, obtaining optimal global plans is intractable, we therefore introduce two simple and efficient distributed approximation algorithms to achieve plan coordination.The first algorithm can be used as a d-approximation of a globally optimal plan for the agents, where d is the depth of the original task, i.e. the length of the longest chain in the set of precedence constraints constituting the task. This algorithm assumes almost no knowledge about the distribution of tasks over the agents. If such knowledge, however, is available, a second, more refined algorithm can be used, that is based on elaborate inter-agent negotiation and is able to achieve a better approximation ratio.
Abstract Many day-to-day situations involve decision making: for example, a taxi company has some... more Abstract Many day-to-day situations involve decision making: for example, a taxi company has some transportation tasks to be carried out, a large firm has to distribute a lot of complicated tasks among its subdivisions or subcontractors, and an air-traffic controller has to assign time slots to planes that are landing or taking off. Intelligent agents can aid in
We consider a model-based diagnosis approach to the diagnosis of plans. Here, a plan performed by... more We consider a model-based diagnosis approach to the diagnosis of plans. Here, a plan performed by some agent(s) is considered as a system to be diagnosed. We introduce a simple formal model of plans and plan execution where it is assumed that the execution of a plan can be monitored by making partial observations of plan states. These observed states are used to compare them with states predicted based on (normal) plan execution. Deviations between observed and predicted states can be explained by qualifying some plan steps in the plan as behaving abnormally. A diagnosis is a subset of plan steps qualified as abnormal that can be used to restore the compatibility between the predicted and the observed partial state. Besides minimum and subset minimal diagnoses, we argue that in plan-based diagnosis maximum informative diagnoses should be considered as preferred diagnoses, too. The latter ones are diagnoses that make the strongest predictions with respect to partial states to be observed in the future. We show that in contrast to minimum diagnoses, finding a (subset minimal) maximum informative diagnosis can be achieved in polynomial time. Finally, we show how these diagnoses can be found efficiently if the plan is distributed over a number of agents.
We consider planning problems where a number of non-cooperative agents have to work on a joint pr... more We consider planning problems where a number of non-cooperative agents have to work on a joint problem. Such problems consist in completing a set of interdependent, hierarchically ordered tasks. Each agent is assigned a subset of tasks to perform for which it has to construct a plan. Since the agents are non-cooperative, they insist on planning independently and do not want to revise their individual plans when the joint plan has to be assembled from the individual plans. We present a general formal framework to study some computational aspects of this non-cooperative coordination problem and we establish some complexity results to identify some of the factors that contribute to the complexity of this problem. Finally, we illustrate our approach with an application to coordination in multi-modal logistic planning.
Uploads
Papers by Cees Witteveen