It is critical for agents in a multiagent environment to avoid interfering with each other when carrying out their tasks. However, to avoid execution ine#ciencies, they also should capitalize on cooperative opportunities. In state oriented domains [14], identifying overlapping e#ects between agents' plans enables some agents to leave some tasks to others, thereby reducing the cost of execution and improving the overall e#ciency of the multiagent system. This is what we term synergy. In this paper, we define criteria for finding a certain type of synergy involving agents with overlapping goals. We also develop algorithms for discovering this synergy between planning agents that exploit hierarchical plan representations. Our results show that our approach not only can reduce the costs of finding synergies compared to non-hierarchical strategies, but can also find synergies that might otherwise be missed.

We discuss a rigorous unifying framework for both planning and replanning, extending an existing logic-based approach to resource-based planning. The primitive concepts in this Action Resource Framework (ARF) are actions and resources. Actions consume and produce resources. Plans are structures composed of actions, resource facts and an explicit dependency function specifying their interrelationships. In this framework, both planning and replanning are conceived as plan transformation processes accomplished by applying sequences of operations on plans. For this, we introduce operators for plan transformation and define the concept of a plan library. Using a refinement planning template, we show how some existing (re)planning methods and heuristics can be described as special cases of this framework. The advantage of the framework is that it offers a unifying view on planning and replanning. 1.

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

Abstract—We present an approach to plan representation in multi-actors scenarios that is suitable for flexible replanning and plan revision purposes. The key idea of the presented approach is in integration of (i) the results of an arbitrary HTN (hierarchical task network)-oriented planner with (ii) the concept of commitments, as a theoretically studied formalism representing mutual relations among intentions of collaborating agents. The paper presents formal model of recursive form of commitments and discusses how it can be deployed to a selected hierarchical planning scenario 1. I.

Multi-agent planning involves finding a plan for each agent in a group where the goals, the actions and the initial resources are distributed over these autonomous agents. An algorithm is given for multi-agent planning that constructs coordinated agent plans distributedly. Instead of designing a planning algorithm and a coordination method separately, this algorithm integrates planning and coordination. The presented multi-agent planning algorithm is based on the idea of several forward heuristic planners that run in parallel. These planners are coordinated by communicating side-products and services via a blackboard.

Abstract. An important aspect of agents is how they construct a plan to reach their goals. Since most agents live in a dynamic environment, they also will often be confronted with situations in which the plans they constructed to reach their goals are no longer feasible. In such situations, agents have to change their plan to deal with the new environment. In this paper we describe such a replanning process using a computational framework, consisting of resources and actions to represent the planned activities of an agent. 1

Abstract We discuss a resource-based planning framework where agents are able to merge plans by exchanging resources. In this framework, plans are specified as structured objects composed of resource consuming and resource producing processes (actions). A plan itself can also be conceived as a process consuming input resources and producing output resources. A plan can be improved if we can remove actions from it while maintaining goal realizability. We describe a reduction property that specifies how one agent can improve its plan by using (free) resources from another agent in such a way that goal realizability is preserved. The plan-merging algorithm we use to specify plan merging in a multi-agent context is an iterative, distributed, any-time application of this reduction property. The performance of this algorithm has been evaluated using a planning data set obtained from a taxi company. The quality of the algorithm is measured by the decrease of the total distance driven by all taxis. By allowing passengers to share rides, we create a trade-off between the additional travel time of passengers and the total drive distance. Allowing passengers to be a few minutes later at their destination and share rides, a significant improvement of the plans can be obtained (from 5 % up to 30 % reduction of the taxi driving distance). 1

RoCTL * was proposed to model robustness in concurrent systems. RoCTL * extended CTL * with the addition of Obligatory and Robustly operators, which quantify over failure-free paths and paths with one more failure respectively. Whether RoCTL * is more expressive than CTL * has remained an open problem since the RoCTL * logic was proposed. We use the equivalence of LTL to counter-free automata to show that RoCTL * is expressively equivalent to CTL*; the translation to CTL * provides the first model checking procedure for RoCTL*. However, we show that RoCTL * is relatively succinct as all satisfaction preserving translations into CTL * are non-elementary in length. Draft:

Abstract not found

We present an approach to distributed planning and coordination architecture for dynamic non-deterministic multiactor mixed-initiative environment. The system provides flexible planning, replanning, and task allocation. The key idea of the presented approach is in integration of (i) I-X hierarchical planner with (ii) agent-based architecture and (iii) commitment-based plan representation. The implementation of the system was verified and evaluated on simulated environment. The experimental validation confirms the performance, stability, and robustness of the system in complex scenarios. 1.