Oscar Sapena

FMAP: Distributed cooperative multi-agent planning

This paper proposes FMAP (Forward Multi-Agent Planning), a fully-distributed multi-agent planning method that integrates planning and coordination. Although FMAP is specifically aimed at solving problems that require cooperation among agents, the flexibility of the domain-independent planning model allows FMAP to tackle multi-agent planning tasks of any type. In FMAP, agents jointly explore the plan space by building up refinement plans through a complete and flexible forward-chaining partial-order planner. The search is guided by hDTG, a novel heuristic function that is based on the concepts of Domain Transition Graph and frontier state and is optimized to evaluate plans in distributed environments. Agents in FMAP apply an advanced privacy model that allows them to adequately keep private information while communicating only the data of the refinement plans that is relevant to each of the participating agents. Experimental results show that FMAP is a general-purpose approach that efficiently solves tightly-coupled domains that have specialized agents and cooperative goals as well as loosely-coupled problems. Specifically, the empirical evaluation shows that FMAP outperforms current MAP systems at solving complex planning tasks that are adapted from the International Planning Competition benchmarks.

Planning and scheduling in an e-learning environment. A constraint-programming-based approach

AI planning techniques offer very appealing possibilities for their application to e-learning environments. After all, dealing with course designs, learning routes and tasks keeps a strong resemblance with a planning process and its main components aimed at finding which tasks must be done and when. This paper focuses on planning learning routes under a very expressive constraint programming approach for planning. After presenting the general planning formulation based on constraint programming, we adapt it to an e-learning setting. This requires to model learners profiles, learning concepts, how tasks attain concepts at different competence levels, synchronisation constraints for working-group tasks, capacity resource constraints, multi-criteria optimisation, breaking symmetry problems and designing particular heuristics. Finally, we also present a simple example (modelled by means of an authoring tool that we are currently implementing) which shows the applicability of this model, the use of different optimisation metrics, heuristics and how the resulting learning routes can be easily generated.

A distributed CSP approach for collaborative planning systems

Distributed or multi-agent planning extends classical AI planning to domains where several agents can plan and act together. There exist many recent developments in this discipline that range over different approaches for distributed planning algorithms, distributed plan execution processes or communication protocols among agents. One of the key issues about distributed planning is that it is the most appropriate way to tackle certain kind of planning problems, specially those where a centralized solving is unfeasible. In this paper we present a new planning framework aimed at solving planning problems in inherently distributed domains where agents have a collection of private data which cannot share with other agents. However, collaboration is required since agents are unable to accomplish its own tasks alone or, at least, can accomplish its tasks better when working with others. Our proposal motivates a new planning scheme based on a distributed heuristic search and a constraint programming resolution process.

Planning in highly dynamic environments: an anytime approach for planning under time constraints

Abstract In this paper, we present a novel and domain-independent planner aimed at working in highly dynamic environments with time constraints. The planner follows the anytime principles: a first solution can be quickly computed and the quality of the final plan is improved as long as time is available. This way, the planner can provide either fast reactions or very good quality plans depending on the demands of the environment. As an on-line planner, it also offers important advantages: our planner allows the plan to start its execution before it is totally generated, unexpected events are efficiently tackled during execution, and sensing actions allow the acquisition of required information in partially observable domains. The planning algorithm is based on problem decomposition and relaxation techniques. The traditional relaxed planning graph has been adapted to this on-line framework by considering information about sensing actions and action costs. Results also show that our planner is competitive with other top-performing classical planners.

A Flexible Coupling Approach to Multi-Agent Planning under Incomplete Information

Multi-agent planning (MAP) approaches are typically oriented at solving loosely coupled problems, being ineffective to deal with more complex, strongly related problems. In most cases, agents work under complete information, building complete knowledge bases. The present article introduces a general-purpose MAP framework designed to tackle problems of any coupling levels under incomplete information. Agents in our MAP model are partially unaware of the information managed by the rest of agents and share only the critical information that affects other agents, thus maintaining a distributed vision of the task. Agents solve MAP tasks through the adoption of an iterative refinement planning procedure that uses single-agent planning technology. In particular, agents will devise refinements through the partial-order planning paradigm, a flexible framework to build refinement plans leaving unsolved details that will be gradually completed by means of new refinements. Our proposal is supported with the implementation of a fully operative MAP system and we show various experiments when running our system over different types of MAP problems, from the most strongly related to the most loosely coupled.

Domain-Independent Online Planning for STRIPS Domains

SimPlanner is an integrated tool for planning and execution-monitoring which allows to interleave planning and execution. In this paper we present the on-line planner incorporated in SimPlanner. This is a domain-independent planner for STRIPS domains. SimPlanner participated in the IPC 2002, obtaining very competitive results.

SimPlanner: An Execution-Monitoring System for Replanning in Dynamic Worlds

In this paper we present SimPlanner, an integrated planning and execution-monitoring system. SimPlanner allows the user to monitor the execution of a plan, interrupt this monitoring process to introduce new information from the world and repair the plan to get it adapted to the new situation.

Reactive planning simulation in dynamic environments with VirtualRobot

One of the difficulties of using Artificial Neural Networks (ANNs) to estimate atmospheric temperature is the large number of potential input variables available. In this study, four different feature extraction methods were used to reduce the input vector to train four networks to estimate temperature at different atmospheric levels. The four techniques used were: genetic algorithms (GA), coefficient of determination (CoD), mutual information (MI) and simple neural analysis (SNA). The results demonstrate that of the four methods used for this data set, mutual information and simple neural analysis can generate networks that have a smaller input parameter set, while still maintaining a high degree of accuracy.

Handling Numeric Criteria in Relaxed Planning Graphs

Nowadays, one of the main techniques used in heuristic planning is the generation of a relaxed planning graph, based on a Graph-plan-like expansion. Planners like FF or MIPS use this type of graphs in order to compute distance-based heuristics during the planning process. This paper presents a new approach to extend the functionality of these graphs in order to manage numeric optimization criteria (problem metric), instead of only plan length optimization. This extension leads to more informed relaxed plans, without increasing significantly the computational cost. Planners that use the relaxed plans for further refinements can take advantage of this additional information to compute better quality plans.

A Planning Tool for Minimizing Reshuffles in Container Terminals

One of the more important problems in container terminal is related to the Container Stacking Problem. A container stack is a type of temporary store where containers await further transport by truck, train or vessel. The main efficiency problem for an individual stack is to ensure easy access to containers at the expected time of transfer. Since stacks are ’last-in, first-out’, and the cranes used to relocate containers within the stack are heavily used, the stacks must be maintained in a state that minimizes on-demand relocations. In this paper, we present a new domain-dependent planning heuristic for finding the best configuration of containers in a bay. Thus, given a set of outgoing containers, our planner minimizes the number of relocations of containers in order to allocate all selected containers in an appropriate order to avoid further reshuffles.