Planning through Automatic Portfolio Configuration: The PbP Approach

In the field of domain-independent planning, several powerful planners implementing different techniques have been developed. However, no one of these systems outperforms all others in every known benchmark domain. In this work, we propose a multi-planner approach that automatically configures a portfolio of planning techniques for each given domain. The configuration process for a given domain uses a set of training instances to: (i) compute and analyze some alternative sets of macro-actions for each planner in the portfolio identifying a (possibly empty) useful set, (ii) select a cluster of planners, each one with the identified useful set of macro-actions, that is expected to perform best, and (iii) derive some additional information for configuring the execution scheduling of the selected planners at planning time. The resulting planning system, called PbP (Portfolio- based Planner), has two variants focusing on speed and plan quality. Different versions of PbP entered and won the learning track of the sixth and seventh International Planning Competitions. In this paper, we experimentally analyze PbP considering planning speed and plan quality in depth. We provide a collection of results that help to understand PbP�s behavior, and demonstrate the effectiveness of our approach to configuring a portfolio of planners with macro-actions

Identifying and Exploiting Features for Effective Plan Retrieval in Case-Based Planning

Case-Based planning can fruitfully exploit knowledge gained by solving a large number of problems, storing the corresponding solutions in a plan library and reusing them for solving similar planning problems in the future. Case-based planning is extremely effective when similar reuse candidates can be efficiently chosen. In this paper, we study an innovative technique based on planning problem features for efficiently retrieving solved planning problems (and relative plans) from large plan libraries. A problem feature is a characteristic of the instance that can be automatically derived from the problem specification, domain and search space analyses, and different problem encodings. Since the use of existing planning features are not always able to effectively distinguish between problems within the same planning domain, we introduce a new class of features. An experimental analysis in this paper shows that our features-based retrieval approach can significantly improve the performance of a state-of-the-art case-based planning system

Portfolio Methods for Optimal Planning: an Empirical Analysis

Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning. Here, we consider the construction of sequential planner portfolios for (domain- independent) optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive experimental analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation

Static and Dynamic Portfolio Methods for Optimal Planning: An Empirical Analysis

Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning. Here, we consider the construction of sequential planner portfolios for domainindependent optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive empirical analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation

FOND planning for pure-past linear temporal logic goals

Recently, Pure-Past Temporal Logic (PPLTL) has proven highly effective in specifying temporally extended goals in deterministic planning domains. In this paper, we show its effectiveness also for fully observable nondeterministic (FOND) planning, both for strong and strong-cyclic plans. We present a notably simple encoding of FOND planning for PPLTL goals into standard FOND planning for final-state goals. The encoding only introduces few fluents (at most linear in the PPLTL goal) without adding any spurious action and allows planners to lazily build the relevant part of the deterministic automaton for the goal formula on-the-fly during the search. We formally prove its correctness, implement it in a tool called Plan4Past, and experimentally show its practical effectiveness

Language Models for Hierarchical Classification of Radiology Reports with Attention Mechanisms, BERT and GPT-4

Radiology reports are a valuable source of textual information used to improve clinical care and support research. In recent years, deep learning techniques have been shown to be effective in classifying radiology reports. This article investigates the use of deep learning techniques with attention mechanisms to achieve better performance in the classification of radiology reports.We focus on various Natural Language Processing approaches, such as LSTM with Attention, BERT, and GPT-4, evaluated on a chest tomography report dataset regarding neoplastic diseases collected from an Italian hospital. In particular, we compare the results with a previous machine learning system, showing that models based on attention mechanisms can achieve higher performance. The Attention Mechanism allows us to identify the most relevant bits of text used by the model to make its predictions. We show that our model achieves state-of-the-art results on the hierarchical classification of radiology reports. Moreover, we evaluate the performance of GPT-4 on the classification of these reports in a zero-shot setup through prompt engineering, showing interesting results even with a small context and a non-English language. Our findings suggest that deep learning techniques with attention mechanisms may be successful in the classification of radiology reports even in non-English languages for which it is not possible to leverage on large text corpus

Recurrent Neural Networks for Daily Estimation of COVID-19 Prognosis with Uncertainty Handling

Most ML-based applications for COVID-19 assess the general conditions of a patient trained and tested on cohorts of patients collected over a short period of time and are capable of providing an alarm a few days in advance, helping clinicians in emergency situations, monitor hospitalised patients and identify potentially critical situations at an early stage. However, the pandemic continues to evolve due to new variants, treatments, and vaccines; considering datasets over short periods could not capture this aspect. In addition, these applications often avoid dealing with the uncertainty associated with the prediction provided by machine learning models, potentially causing costly mistakes. In this work, we present a system based on Recurrent Neural Networks (RNN) for the daily estimate of the prognosis of COVID-19 patients that is built and tested using data collected over a long period of time. Our system achieves high predictive performance and uses an algorithm to effectively determine and discard those patients for whom RNN cannot predict the prognosis with sufficient confidence

Exploiting Macro-actions and Predicting Plan Length in Planning as Satisfiability

The use of automatically learned knowledge for a planning domain can significantly improve the performance of a generic planner when solving a problem in this domain. In this work, we focus on the well-known SAT-based approach to planning and investigate two types of learned knowledge that have not been studied in this planning framework before: macro-actions and planning horizon. Macro-actions are sequences of actions that typically occur in the solution plans, while a planning horizon of a problem is the length of a (possibly optimal) plan solving it. We propose a method that uses a machine learning tool for building a predictive model of the optimal planning horizon, and variants of the well-known planner SatPlan and solver MiniSat that can exploit macro actions and learned planning horizons to improve their performance. An experimental analysis illustrates the effectiveness of the proposed techniques