Model-Lite Case-Based Planning

There is increasing awareness in the planning community that depending on complete models impedes the applicability of planning technology in many real world domains where the burden of specifying complete domain models is too high. In this paper, we consider a novel solution for this challenge that combines generative planning on incomplete domain models with a library of plan cases that are known to be correct. While this was arguably the original motivation for case-based planning, most existing case-based planners assume (and depend on) from-scratch planners that work on complete domain models. In contrast, our approach views the plan generated with respect to the incomplete model as a "skeletal plan" and augments it with directed mining of plan fragments from library cases. We will present the details of our approach and present an empirical evaluation of our method in comparison to a state-of-the-art case-based planner that depends on complete domain models

E-Learning and Intelligent Planning: Improving Content Personalization

Combining learning objects is a challenging topic because of its direct application to curriculum generation, tailored to the students' profiles and preferences. Intelligent planning allows us to adapt learning routes (i.e. sequences of learning objects), thus highly improving the personalization of contents, the pedagogical requirements and specific necessities of each student. This paper presents a general and effective approach to extract metadata information from the e-learning contents, a form of reusable learning objects, to generate a planning domain in a simple, automated way. Such a domain is used by an intelligent planner that provides an integrated recommendation system, which adapts, stores and reuses the best learning routes according to the students' profiles and course objectives. If any inconsistency happens during the route execution, e.g. the student fails to pass an assessment test which prevents him/her from continuing the natural course of the route, the systeGarrido, A.; Morales, L. (2014). E-Learning and Intelligent Planning: Improving Content Personalization. IEEE Revista Iberoamericana de Tecnologías del Aprendizaje. 9(1):1-7. doi:10.1109/RITA.2014.2301886S179

A case-based approach to heuristic planning

Most of the great success of heuristic search as an approach to AI Planning is due to the right design of domain-independent heuristics. Although many heuristic planners perform reasonably well, the computational cost of computing the heuristic function in every search node is very high, causing the planner to scale poorly when increasing the size of the planning tasks. For tackling this problem, planners can incorporate additional domain-dependent heuristics in order to improve their performance. Learning-based planners try to automatically acquire these domain-dependent heuristics using previous solved problems. In this work, we present a case-based reasoning approach that learns abstracted state transitions that serve as domain control knowledge for improving the planning process. The recommendations from the retrieved cases are used as guidance for pruning or ordering nodes in different heuristic search algorithms applied to planning tasks. We show that the CBR guidance is appropriate for a considerable number of planning benchmarks.This work has been partially supported by the Spanish MEC projects PELEA: TIN2008-6701-C03-03 and PlanInteraction: TIN2011-27652-C03-02.Publicad

On the use of case-based planning for e-learning personalization

This is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, 60, 1-15, 2016. DOI:10.1016/j.eswa.2016.04.030In this paper we propose myPTutor, a general and effective approach which uses AI planning techniques to create fully tailored learning routes, as sequences of Learning Objects (LOs) that fit the pedagogical and students’ requirements. myPTutor has a potential applicability to support e-learning personalization by producing, and automatically solving, a planning model from (and to) e-learning standards in a vast number of real scenarios, from small to medium/large e-learning communities. Our experiments demonstrate that we can solve scenarios with large courses and a high number of students. Therefore, it is perfectly valid for schools, high schools and universities, especially if they already use Moodle, on top of which we have implemented myPTutor. It is also of practical significance for repairing unexpected discrepancies (while the students are executing their learning routes) by using a Case-Based Planning adaptation process that reduces the differences between the original and the new route, thus enhancing the learning process. © 2016 Elsevier Ltd. All rights reserved.This work has been partially funded by the Consolider AT project CSD2007-0022 INGENIO 2010 of the Spanish Ministry of Science and Innovation, the MICINN project TIN2011-27652-C03-01, the MINECO and FEDER project TIN2014-55637-C2-2-R, the Mexican National Council of Science and Technology, the Valencian Prometeo project II/2013/019 and the BW5053 research project of the Free University of Bozen-Bolzano.Garrido Tejero, A.; Morales, L.; Serina, I. (2016). On the use of case-based planning for e-learning personalization. Expert Systems with Applications. 60:1-15. https://doi.org/10.1016/j.eswa.2016.04.030S1156

On-line case-based policy learning for automated planning in probabilistic environments

Many robotic control architectures perform a continuous cycle of sensing, reasoning and acting, where that reasoning can be carried out in a reactive or deliberative form. Reactive methods are fast and provide the robot with high interaction and response capabilities. Deliberative reasoning is particularly suitable in robotic systems because it employs some form of forward projection (reasoning in depth about goals, pre-conditions, resources and timing constraints) and provides the robot reasonable responses in situations unforeseen by the designer. However, this reasoning, typically conducted using Artificial Intelligence techniques like Automated Planning (AP), is not effective for controlling autonomous agents which operate in complex and dynamic environments. Deliberative planning, although feasible in stable situations, takes too long in unexpected or changing situations which require re-planning. Therefore, planning cannot be done on-line in many complex robotic problems, where quick responses are frequently required. In this paper, we propose an alternative approach based on case-based policy learning which integrates deliberative reasoning through AP and reactive response time through reactive planning policies. The method is based on learning planning knowledge from actual experiences to obtain a case-based policy. The contribution of this paper is two fold. First, it is shown that the learned case-based policy produces reasonable and timely responses in complex environments. Second, it is also shown how one case-based policy that solves a particular problem can be reused to solve a similar but more complex problem in a transfer learning scope.This paper has been partially supported by the Spanish Ministerio de Econom a y Competitividad TIN2015-65686-C5-1-R and the European Union's Horizon 2020 Research and Innovation programme under Grant Agreement No. 730086 (ERGO)

A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making

The field of Sequential Decision Making (SDM) provides tools for solving Sequential Decision Processes (SDPs), where an agent must make a series of decisions in order to complete a task or achieve a goal. Historically, two competing SDM paradigms have view for supremacy. Automated Planning (AP) proposes to solve SDPs by performing a reasoning process over a model of the world, often represented symbolically. Conversely, Reinforcement Learning (RL) proposes to learn the solution of the SDP from data, without a world model, and represent the learned knowledge subsymbolically. In the spirit of reconciliation, we provide a review of symbolic, subsymbolic and hybrid methods for SDM. We cover both methods for solving SDPs (e.g., AP, RL and techniques that learn to plan) and for learning aspects of their structure (e.g., world models, state invariants and landmarks). To the best of our knowledge, no other review in the field provides the same scope. As an additional contribution, we discuss what properties an ideal method for SDM should exhibit and argue that neurosymbolic AI is the current approach which most closely resembles this ideal method. Finally, we outline several proposals to advance the field of SDM via the integration of symbolic and subsymbolic AI

Kernel functions for case-based planning

Case-based planning can take advantage of former problem-solving experiences by storing in a plan library previously generated plans that can be reused to solve similar planning problems in the future. Although comparative worst-case complexity analyses of plan generation and reuse techniques reveal that it is not possible to achieve provable efficiency gain of reuse over generation, we show that the case-based planning approach can be an effective alternative to plan generation when similar reuse candidates can be chosen. In this paper we describe an innovative case-based planning system, called OAKplan, which can efficiently retrieve planning cases from plan libraries containing more than ten thousand cases, choose heuristically a suitable candidate and adapt it to provide a good quality solution plan which is similar to the one retrieved from the case library. Given a planning problem we encode it as a compact graph structure, that we call Planning Encoding Graph, which gives us a detailed description of the topology of the planning problem. By using this graph representation, we examine an approximate retrieval procedure based on kernel functions that effectively match planning instances, achieving extremely good performance in standard benchmark domains. The experimental results point out the effect of the case base size and the importance of accurate matching functions for global system performance. Overall, we show that OAKplan is competitive with state-of-the-art plan generation systems in terms of number of problems solved, CPU time, plan difference values and plan quality when cases similar to the current planning problem are available in the plan library. © 2010 Elsevier B.V. All rights reserved