A Critical Look at the Abstraction Based on Macro-Operators

Abstraction can be an effective technique for dealing with the complexity of planning tasks. This paper is aimed at assessing and identifying in which cases abstraction can actually speed-up the overall search. In fact, it is well known that the impact of abstraction on the time spent to search for a solution of a planning problem can be positive or negative, depending on several factors -including the number of objects defined in the domain, the branching factor, and the plan length. Experimental results highlight the role of such aspects on the overall performance of an algorithm that performs the search at the ground-level only, and compares them with the ones obtained by enforcing abstraction

An Adaptive Approach for Planning in Dynamic Environments

Planning in a dynamic environment is a complex task that requires several issues to be investigated in order to manage the associated search complexity. In this paper, an adaptive behavior that integrates planning with learning is presented. The former is performed adopting a hierarchical approach, interleaved with execution. The latter, devised to identify new abstract operators, adopts a chunking technique on successful plans. Integration between planning and learning is also promoted by an agent architecture explicitly designed for supporting abstraction

Experimenting Abstraction Mechanisms Through an Agent-Based Hierarchical Planner

In this paper, an agent-based architecture devised to perform experiments on hierarchical planning is described. The planning activity results from the interaction of a community of agents, some of them being explicitly devoted to embed one or more existing planners. The proposed architecture allows to exploit the characteristics of any external planner, under the hypothesis that a suitable wrapper –in form of planning agent– is provided. An implementation of the architecture, able to embed one planner of the graphplan family, has been used to directly assess whether or not abstraction mechanisms can help to reduce the time complexity of the search on specific domains. Some preliminary experiments are reported, focusing on problems taken from the AIPS 2002, 2000 and 1998 planning competitions. Comparative results, obtained by assessing the performances of the selected planner (used first in a stand-alone configuration and then embedded into the proposed multi-agent architecture), put into evidence that abstraction may significantly speed up the search

Personalized Text Categorization Using a MultiAgent Architecture

In this paper, a system able to retrieve contents deemed relevant for the users through a text categorization process, is presented. The system is built exploiting a generic multiagent architecture that supports the implementation of applications aimed at (i) retrieving heterogeneous data spread among different sources (e.g., generic html pages, news, blogs, forums, and databases); (ii) filtering and organizing them according to personal interests explicitly stated by each user; (iii) providing adaptation techniques to improve and refine throughout time the profile of each selected user. In particular, the implemented multiagent system creates personalized press-revies from online newspapers. Preliminary results are encouraging and highlight the effectiveness of the approach

Developing an ML pipeline for asthma and COPD: The case of a Dutch primary care service

A complex combination of clinical, demographic and lifestyle parameters determines the correct diagnosis and the most effective treatment for asthma and Chronic Obstructive Pulmonary Disease patients. Artificial Intelligence techniques help clinicians in devising the correct diagnosis and designing the most suitable clinical pathway accordingly, tailored to the specific patient conditions. In the case of machine learning (ML) approaches, availability of real-world patient clinical data to train and evaluate the ML pipeline deputed to assist clinicians in their daily practice is crucial. However, it is common practice to exploit either synthetic data sets or heavily preprocessed collections cleaning and merging different data sources. In this paper, we describe an automated ML pipeline designed for a real-world data set including patients from a Dutch primary care service, and provide a performance comparison of different prediction models for (i) assessing various clinical parameters, (ii) designing interventions, and (iii) defining the diagnosis

Higher and lower supramolecular orders for the design of self-assembled heterochiral tripeptide hydrogel biomaterials

The self-assembly behaviour of the eight stereoisomers of Val\u2013Phe\u2013Phe tripeptides under physiological conditions is assessed by several spectroscopy and microscopy techniques. We report the first examples of self-organised hydrogels from tripeptides in the L\u2013D\u2013L or D\u2013L\u2013D configuration, besides the expected gels with the D\u2013L\u2013L or L\u2013D\u2013D configuration, thus widening the scope for using amino acid chirality as a tool to drive self-assembly. Importantly, the positions of D- and L-amino acids in the gelling tripeptides determine a higher or lower supramolecular order, which translates into macroscopic gels with different rheological properties and thermal behaviours. The more durable hydrogels perform well in cytotoxicity assays, and also as peptides in solution. An appropriate design of the chirality of self-assembling sequences thus allows for the fine-tuning of the properties of the gel biomaterials. In conclusion, this study adds key details of supramolecular organization that will assist in the ex novo design of assembling chiral small molecules for their use as biomaterials