Planning with preferences has been employed extensively to quickly generate high-quality plans. However, it may be difficult for the human expert to supply this information without knowledge of the reasoning employed by the planner and the distribution of planning problems. We consider the problem of actively eliciting preferences from a human expert during the planning process. Specifically, we study this problem in the context of the Hierarchical Task Network (HTN) planning framework as it allows easy interaction with the human. Our experimental results on several diverse planning domains show that the preferences gathered using the proposed approach improve the quality and speed of the planner, while reducing the burden on the human expert.
Our relates closely to mixed-initiative planning @cite_5 @cite_28 @cite_34 , which interleaves planning by the expert with automated planning. However, they are conceptually different in the expert's role and intervention in the planning process. Mixed-initiative planning is a negotiation between an agent and human on mutable goals sub-goals, and partial plans and intervention can be initiated by either party. On the other hand, is responsible for only acquiring expert knowledge wherever it is expected to be most useful. builds on the HTN planning framework since it allows the human to encode coarse knowledge and subsequently learn fine-grained knowledge via interaction.
@cite_28 @cite_5 @cite_34
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We have been examining mixed-initiative planning systems in the context of command and control or logistical overview situations. In such environments, the human and the computer must work together in a very tightly coupled way to solve problems that neither alone could manage. In this paper, we describe our implementation of a prototype version of such a system, TRAINS-95, which helps a manager solve routing problems in a simple transportation domain. Interestingly perhaps, traditional planning technology does not play a major role in the system, and in fact it is difficult to see how such components might fit into a mixed-initiative system. We describe some of these issues, and present our agenda for future research into mixed-initiative plan reasoning. At this writing, the TRAINS-95 system has been used by more than 100 people to solve simple problems at various conferences and workshops, and in our experiments. Multiagent planning deals with the problem of generating plans for multiple agents. It requires formalizing ways for the agents to interact and cooperate, in order to achieve their goals. One way for the agents to interact is through negotiations. Integration of negotiation in multiagent planning has not been extensively investigated and a systematic way for this task has yet to be found. We develop a generic model for negotiation in dynamic environments and apply it to generate joint-plans with negotiation for multiple agents. We identify the minimal requirements for such a model and propose a general scheme for one-to-one negotiations. This model of negotiation is instantiated to deal with dynamic knowledge of planning agents. We demonstrate how logic programming can be employed as a uniform platform to support both planning and negotiation, providing an ideal testbed for experimenting with multiagent planning with negotiations. This contribution proposes a model for argumentation-based multi-agent planning, with a focus on cooperative scenarios. It consists in a multi-agent extension of DeLP-POP, partial order planning on top of argumentation-based defeasible logic programming. In DeLP-POP, actions and arguments (combinations of rules and facts) may be used to enforce some goal, if their conditions (are known to) apply and arguments are not defeated by other arguments applying. In a cooperative planning problem a team of agents share a set of goals but have diverse abilities and beliefs. In order to plan for these goals, agents start a stepwise dialogue consisting of exchanges of plan proposals, plus arguments against them. Since these dialogues instantiate an A* search algorithm, these agents will find a solution if some solution exists, and moreover, it will be provably optimal (according to their knowledge). Abstract In order to model plan coordination behavior of agents we develop a simple framework for representing plans, resources and goals of agents. Plans are represented as directed acyclic graphs of skills and resources that, given adequate initial resources, can realize special resources, called goals. Given the storage costs of resources, application costs of skills, and values of goals, it is possible to reason about the profits of a plan for an agent. We then model two forms of plan coordination behavior between two agents, viz. fusion , aiming at the maximization of the total yield of the agents involved, and collaboration , which aims at the maximization of the individual yield of each agent. We argue how both forms of cooperation can be seen as iterative plan revision processes. We also present efficient polynomial algorithms for agent plan fusion and collaboration that are based on this idea of iterative plan revision. Both the framework and the fusion algorithm will be illustrated by an example from the field of transportation, where agents are transportation companies.