@conference{Ceni2023,

title = { Improving Fairness via Intrinsic Plasticity in Echo State Networks },

author = {Andrea Ceni and Davide Bacciu and Valerio De Caro and Claudio Gallicchio and Luca Oneto

},

editor = {Michel Verleysen},

year  = {2023},

date = {2023-10-04},

urldate = {2023-10-04},

booktitle = {Proceedings of the 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Cossu2023,

title = { A Protocol for Continual Explanation of SHAP },

author = {Andrea Cossu and Francesco Spinnato and Riccardo Guidotti and Davide Bacciu},

editor = {Michel Verleysen},

year  = {2023},

date = {2023-10-04},

urldate = {2023-10-04},

booktitle = {Proceedings of the 31th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Caro2022,

title = {Federated Adaptation of Reservoirs via Intrinsic Plasticity},

author = {Valerio {De Caro} and Claudio Gallicchio and Davide Bacciu},

editor = {Michel Verleysen},

url = {https://arxiv.org/abs/2206.11087, Arxiv},

year  = {2022},

date = {2022-10-05},

urldate = {2022-10-05},

booktitle = {Proceedings of the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning  (ESANN 2022)},

abstract = {We propose a novel algorithm for performing federated learning with Echo State Networks (ESNs) in a client-server scenario. In particular, our proposal focuses on the adaptation of reservoirs by combining Intrinsic Plasticity with Federated Averaging. The former is a gradient-based method for adapting the reservoir's non-linearity in a local and unsupervised manner, while the latter provides the framework for learning in the federated scenario. We evaluate our approach on real-world datasets from human monitoring, in comparison with the previous approach for federated ESNs existing in literature. Results show that adapting the reservoir with our algorithm provides a significant improvement on the performance of the global model. },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Matteoni2022,

title = {Continual Learning for Human State Monitoring},

author = {Federico Matteoni and Andrea Cossu and Claudio Gallicchio and Vincenzo Lomonaco and Davide Bacciu},

editor = {Michel Verleysen},

url = {https://arxiv.org/pdf/2207.00010, Arxiv},

year  = {2022},

date = {2022-10-05},

urldate = {2022-10-05},

booktitle = {Proceedings of the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2022)},

abstract = {Continual Learning (CL) on time series data represents a promising but under-studied avenue for real-world applications. We propose two new CL benchmarks for Human State Monitoring. We carefully designed the benchmarks to mirror real-world environments in which new subjects are continuously added. We conducted an empirical evaluation to assess the ability of popular CL strategies to mitigate forgetting in our benchmarks. Our results show that, possibly due to the domain-incremental properties of our benchmarks, forgetting can be easily tackled even with a simple finetuning and that existing strategies struggle in accumulating knowledge over a fixed, held-out, test subject.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Lanciano2021,

title = { Predictive Auto-scaling with OpenStack Monasca},

author = {Giacomo Lanciano and Filippo Galli and Tommaso Cucinotta and Davide Bacciu and Andrea Passarella},

url = {https://arxiv.org/abs/2111.02133, Arxiv},

doi = {10.1145/3468737.3494104},

year  = {2021},

date = {2021-12-06},

urldate = {2021-12-06},

booktitle = {Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing (UCC 2021)},

pages = {1-10},

abstract = {Cloud auto-scaling mechanisms are typically based on reactive automation rules that scale a cluster whenever some metric, e.g., the average CPU usage among instances, exceeds a predefined threshold. Tuning these rules becomes particularly cumbersome when scaling-up a cluster involves non-negligible times to bootstrap new instances, as it happens frequently in production cloud services.

To deal with this problem, we propose an architecture for auto-scaling cloud services based on the status in which the system is expected to evolve in the near future. Our approach leverages on time-series forecasting techniques, like those based on machine learning and artificial neural networks, to predict the future dynamics of key metrics, e.g., resource consumption metrics, and apply a threshold-based scaling policy on them. The result is a predictive automation policy that is able, for instance, to automatically anticipate peaks in the load of a cloud application and trigger ahead of time appropriate scaling actions to accommodate the expected increase in traffic.

We prototyped our approach as an open-source OpenStack component, which relies on, and extends, the monitoring capabilities offered by Monasca, resulting in the addition of predictive metrics that can be leveraged by orchestration components like Heat or Senlin. We show experimental results using a recurrent neural network and a multi-layer perceptron as predictor, which are compared with a simple linear regression and a traditional non-predictive auto-scaling policy. However, the proposed framework allows for the easy customization of the prediction policy as needed. },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{BacciuCAIP2021,

title = {Towards Functional Safety Compliance of Recurrent Neural Networks},

author = {Davide Bacciu and Antonio Carta and Daniele Di Sarli and Claudio Gallicchio and Vincenzo Lomonaco and Salvatore Petroni},

url = {https://aiforpeople.org/conference/assets/papers/CAIP21-P09.pdf, Open Access PDF},

year  = {2021},

date = {2021-11-20},

booktitle = {Proceedings of the International Conference on AI for People (CAIP 2021)},

abstract = {Deploying Autonomous Driving systems requires facing some novel challenges for the Automotive industry. One of the most critical aspects that can severely compromise their deployment is Functional Safety. The ISO 26262 standard provides guidelines to ensure Functional Safety of road vehicles. However, this standard is not suitable to develop Artificial Intelligence

based systems such as systems based on Recurrent Neural Networks (RNNs). To address this issue, in this paper we propose a new methodology, composed of three steps. The first step is the robustness evaluation of the RNN against inputs perturbations. Then, a proper set of safety measures must be defined according to the model’s robustness, where less robust models will require stronger mitigation. Finally, the functionality of the entire system must be extensively tested

according to Safety Of The Intended Functionality (SOTIF) guidelines, providing quantitative results about the occurrence of unsafe scenarios, and by evaluating appropriate Safety Performance Indicators.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Cossu2021,

title = { Continual Learning with Echo State Networks },

author = {Andrea Cossu and Davide Bacciu and Antonio Carta and Claudio Gallicchio and Vincenzo Lomonaco},

editor = {Michel Verleysen},

url = {https://arxiv.org/abs/2105.07674, Arxiv},

doi = {10.14428/esann/2021.ES2021-80},

year  = {2021},

date = {2021-10-06},

urldate = {2021-10-06},

booktitle = {Proceedings of the 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning  (ESANN 2021)

},

volume = {275-280},

abstract = { Continual Learning (CL) refers to a learning setup where data is non stationary and the model has to learn without forgetting existing knowledge. The study of CL for sequential patterns revolves around trained recurrent networks. In this work, instead, we introduce CL in the context of Echo State Networks (ESNs), where the recurrent component is kept fixed. We provide the first evaluation of catastrophic forgetting in ESNs and we highlight the benefits in using CL strategies which are not applicable to trained recurrent models. Our results confirm the ESN as a promising model for CL and open to its use in streaming scenarios. },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Bacciu2021,

title = {Benchmarking Reservoir and Recurrent Neural Networks for Human State and Activity Recognition},

author = {Davide Bacciu and Daniele Di Sarli and Claudio Gallicchio and Alessio Micheli and Niccolo Puccinelli},

doi = {10.1007/978-3-030-85099-9_14},

year  = {2021},

date = {2021-06-16},

urldate = {2021-06-16},

booktitle = {Proceedings of the 16th International Work Conference on Artificial Neural Networks (IWANN 2021)},

volume = {12862},

pages = {168-179},

publisher = {Springer},

abstract = {Monitoring of human states from streams of sensor data is an appealing applicative area for Recurrent Neural Network (RNN) models. In such a scenario, Echo State Network (ESN) models from the Reservoir Computing paradigm can represent good candidates due to the efficient training algorithms, which, compared to fully trainable RNNs, definitely ease embedding on edge devices.



In this paper, we provide an experimental analysis aimed at assessing the performance of ESNs on tasks of human state and activity recognition, in both shallow and deep setups. Our analysis is conducted in comparison with vanilla RNNs, Long Short-Term Memory, Gated Recurrent Units, and their deep variations. Our empirical results on several datasets clearly indicate that, despite their simplicity, ESNs are able to achieve a level of accuracy that is competitive with those models that require full adaptation of the parameters. From a broader perspective, our analysis also points out that recurrent networks can be a first choice for the class of tasks under consideration, in particular in their deep and gated variants.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{Wcci20CL,

title = {Continual Learning with Gated Incremental Memories for Sequential Data Processing},

author = {Andrea Cossu and Antonio Carta and Davide Bacciu},

url = {https://arxiv.org/pdf/2004.04077.pdf, Arxiv},

doi = {10.1109/IJCNN48605.2020.9207550},

year  = {2020},

date = {2020-07-19},

urldate = {2020-07-19},

booktitle = {Proceedings  of the 2020 IEEE World Congress on Computational Intelligence},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{ecml2020LMN,

title = {Incremental training of a recurrent neural network exploiting a multi-scale dynamic memory},

author = {Antonio Carta and Alessandro Sperduti and Davide Bacciu},

year  = {2020},

date = {2020-06-05},

urldate = {2020-06-05},

booktitle = {Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2020 (ECML-PKDD 2020)},

publisher = {Springer International Publishing},

abstract = {The effectiveness of recurrent neural networks can be largely influenced by their ability to store into their dynamical memory information extracted from input sequences at different frequencies and timescales. Such a feature can be introduced into a neural architecture by an appropriate modularization of the dynamic memory. In this paper we propose a novel incrementally trained recurrent architecture targeting explicitly multi-scale learning. First, we show how to extend the architecture of a simple RNN by separating its hidden state into different modules, each subsampling the network hidden activations at different frequencies. Then, we discuss a training algorithm where new modules are iteratively added to the model to learn progressively longer dependencies. Each new module works at a slower frequency than the previous ones and it is initialized to encode the subsampled sequence of hidden activations. Experimental results on synthetic and real-world datasets on speech recognition and handwritten characters show that the modular architecture and the incremental training algorithm improve the ability of recurrent neural networks to capture long-term dependencies.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{aistats2020,

title = {A Deep Generative Model for Fragment-Based Molecule Generation},

author = {Marco Podda and Davide Bacciu and Alessio Micheli},

url = {https://arxiv.org/abs/2002.12826},

year  = {2020},

date = {2020-06-03},

urldate = {2020-06-03},

booktitle = {Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020) },

abstract = {Molecule generation is a challenging open problem in cheminformatics. Currently, deep generative approaches addressing the challenge belong to two broad categories, differing in how molecules are represented. One approach encodes molecular graphs as strings of text, and learn their corresponding character-based language model. Another, more expressive, approach operates directly on the molecular graph. In this work, we address two limitations of the former: generation of invalid or duplicate molecules. To improve validity rates, we develop a language model for small molecular substructures called fragments, loosely inspired by the well-known paradigm of Fragment-Based Drug Design. In other words, we generate molecules fragment by fragment, instead of atom by atom. To improve uniqueness rates, we present a frequency-based clustering strategy that helps to generate molecules with infrequent fragments. We show experimentally that our model largely outperforms other language model-based competitors, reaching state-of-the-art performances typical of graph-based approaches. Moreover, generated molecules display molecular properties similar to those in the training sample, even in absence of explicit task-specific supervision.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{ssci19,

title = {Sequential Sentence Embeddings for Semantic Similarity},

author = {Davide Bacciu and Antonio Carta},

doi = {10.1109/SSCI44817.2019.9002824},

year  = {2019},

date = {2019-12-06},

urldate = {2019-12-06},

booktitle = {Proceedings of the 2019 IEEE Symposium Series on Computational Intelligence (SSCI'19)},

publisher = {IEEE},

abstract = {  Sentence embeddings are distributed representations of sentences intended to be general features to be effectively used as input for deep learning models across different natural language processing tasks.

  State-of-the-art sentence embeddings for semantic similarity are computed with a weighted average of pretrained word embeddings, hence completely ignoring the contribution of word ordering within a sentence in defining its semantics. We propose a novel approach to compute sentence embeddings for semantic similarity that exploits a linear autoencoder for sequences. The method can be trained in closed form and it is easy to fit on unlabeled sentences. Our method provides a grounded approach to identify and subtract common discourse from a sentence and its embedding, to remove associated uninformative features. Unlike similar methods in the literature (e.g. the popular Smooth Inverse Frequency approach), our method is able to account for word order. We show that our estimate of the common discourse vector improves the results on two different semantic similarity benchmarks when compared to related approaches from the literature.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{clic2019,

title = {Suitable doesn’t mean attractive. Human-based evaluation of automatically generated headlines},

author = {Michele Cafagna and Lorenzo {De Mattei} and Davide Bacciu and Malvina Nissim},

editor = {Raffaella Bernardi and Roberto Navigli and Giovanni Semeraro},

url = {http://ceur-ws.org/Vol-2481/paper13.pdf},

year  = {2019},

date = {2019-11-15},

urldate = {2019-11-15},

booktitle = {Proceedings of the 6th Italian Conference on Computational Linguistics (CLiC-it 2019)},

volume = {2481 },

publisher = {CEUR},

series = {AI*IA series},

abstract = {We train three different models to generate newspaper headlines from a portion of the corresponding article. The articles are obtained from two mainstream Italian newspapers. In order to assess the models’ performance, we set up a human-based evaluation where 30 different native speakers expressed their judgment over a variety of aspects. The outcome shows that (i) pointer networks perform better than standard sequence to sequence models, creating mostly correct and appropriate titles; (ii) the suitability of a headline to its article for pointer networks is on par or better than the gold headline; (iii) gold headlines are still by far more inviting than generated headlines to read the whole article, highlighting the contrast between human  creativity and content appropriateness.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{lmnArx18,

title = {Linear Memory Networks},

author = {Davide Bacciu and Antonio Carta and Alessandro Sperduti},

url = {https://arxiv.org/pdf/1811.03356.pdf},

doi = {10.1007/978-3-030-30487-4_40},

year  = {2019},

date = {2019-09-17},

urldate = {2019-09-17},

booktitle = {Proceedings of the 28th International Conference on Artificial Neural Networks (ICANN 2019), },

volume = {11727},

pages = {513-525 },

publisher = {Springer-Verlag},

series = {Lecture Notes in Computer Science},

abstract = {Recurrent neural networks can learn complex transduction problems that require maintaining and actively exploiting a memory of their inputs. Such models traditionally consider memory and input-output functionalities indissolubly entangled. We introduce a novel recurrent architecture based on the conceptual separation between the functional input-output transformation and the memory mechanism, showing how they can be implemented through different neural components. By building on such conceptualization, we introduce the Linear Memory Network, a recurrent model comprising a feedforward neural network, realizing the non-linear functional transformation, and a linear autoencoder for sequences, implementing the memory component. The resulting architecture can be efficiently trained by building on closed-form solutions to linear optimization problems. Further, by exploiting equivalence results between feedforward and recurrent neural networks we devise a pretraining schema for the proposed architecture. Experiments on polyphonic music datasets show competitive results against gated recurrent networks and other state of the art models. },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{inns2019,

title = {Deep Tree Transductions - A Short Survey},

author = {Bacciu Davide and Bruno Antonio},

editor = {Luca Oneto and Nicol{`o} Navarin and Alessandro Sperduti and Davide Anguita},

url = {https://arxiv.org/abs/1902.01737},

doi = {10.1007/978-3-030-16841-4_25},

year  = {2019},

date = {2019-01-04},

urldate = {2019-01-04},

booktitle = {Proceedings of the  2019 INNS Big Data and Deep Learning (INNSBDDL 2019) },

pages = {236--245},

publisher = {Springer International Publishing},

series = {Recent Advances in Big Data and Deep Learning},

abstract = {The paper surveys recent extensions of the Long-Short Term Memory networks to handle tree structures from the perspective of learning non-trivial forms of isomorph structured transductions. It provides a discussion of modern TreeLSTM models, showing the effect of the bias induced by the direction of tree processing. An empirical analysis is performed on real-world benchmarks, highlighting how there is no single model adequate to effectively approach all transduction problems.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{ijcnn2017,

title = {DropIn: Making Neural Networks Robust to Missing Inputs by Dropout},

author = {Bacciu Davide and Crecchi Francesco and Morelli Davide},

url = {https://arxiv.org/abs/1705.02643},

doi = {10.1109/IJCNN.2017.7966106},

isbn = {978-1-5090-6182-2},

year  = {2017},

date = {2017-05-19},

urldate = {2017-05-19},

booktitle = {Proceedings  of the 2017 International Joint Conference on Neural Networks (IJCNN 2017) },

pages = {2080-2087},

publisher = {IEEE},

abstract = {The paper presents a novel, principled approach to train recurrent neural networks from the Reservoir Computing family that are robust to missing part of the input features at prediction time. By building on the ensembling properties of Dropout regularization, we propose a methodology, named DropIn, which efficiently trains a neural model as a committee machine of subnetworks, each capable of predicting with a subset of the original input features. We discuss the application of the DropIn methodology in the context of Reservoir Computing models and targeting applications characterized by input sources that are unreliable or prone to be disconnected, such as in pervasive wireless sensor networks and ambient intelligence. We provide an experimental assessment using real-world data from such application domains, showing how the Dropin methodology allows to maintain predictive performances comparable to those of a model without missing features, even when 20%–50% of the inputs are not available.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{fun2016,

title = {An Investigation into Cybernetic Humor, or: Can Machines Laugh?},

author = {Bacciu Davide and Gervasi Vincenzo and Prencipe Giuseppe},

editor = {Erik D. Demaine and Fabrizio Grandoni},

url = {http://drops.dagstuhl.de/opus/volltexte/2016/5882},

doi = {10.4230/LIPIcs.FUN.2016.3},

issn = {1868-8969},

year  = {2016},

date = {2016-06-10},

booktitle = {Proceedings of  the 8th International Conference on Fun with Algorithms (FUN'16) },

volume = {49},

pages = {1-15},

publisher = {Schloss Dagstuhl-Leibniz-Zentrum fuer Informatik},

series = {Leibniz International Proceedings in Informatics (LIPIcs)},

abstract = {The mechanisms of humour have been the subject of much study and investigation, starting with and up to our days. Much of this work is based on literary theories, put forward by some of the most eminent philosophers and thinkers of all times, or medical theories, investigating the impact of humor on brain activity or behaviour. Recent functional neuroimaging studies, for instance, have investigated the process of comprehending and appreciating humor by examining functional activity in distinctive regions of brains stimulated by joke corpora. Yet, there is precious little work on the computational side, possibly due to the less hilarious nature of computer scientists as compared to men of letters and sawbones. In this paper, we set to investigate whether literary theories of humour can stand the test of algorithmic laughter. Or, in other words, we ask ourselves the vexed question: Can machines laugh? We attempt to answer that question by testing whether an algorithm - namely, a neural network - can "understand" humour, and in particular whether it is possible to automatically identify abstractions that are predicted to be relevant by established literary theories about the mechanisms of humor. Notice that we do not focus here on distinguishing humorous from serious statements - a feat that is clearly way beyond the capabilities of the average human voter, not to mention the average machine - but rather on identifying the underlying mechanisms and triggers that are postulated to exist by literary theories, by verifying if similar mechanisms can be learned by machines. },

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_588269,

title = {Learning context-aware mobile robot navigation in home environments},

author = {Bacciu Davide and Gallicchio Claudio and Micheli Alessio and Di Rocco Maurizio and Saffiotti Alessandro},

doi = {10.1109/IISA.2014.6878733},

isbn = {9781479961702},

year  = {2014},

date = {2014-01-01},

booktitle = {Proceedings of the 5th International Conference on Information, Intelligence, Systems and Applications (IISA 2014)},

pages = {57--62},

publisher = {IEEE},

abstract = {We present an approach to make planning adaptive in order to enable context-aware mobile robot navigation. We integrate a model-based planner with a distributed learning system based on reservoir computing, to yield personalized planning and resource allocations that account for user preferences and environmental changes. We demonstrate our approach in a real robot ecology, and show that the learning system can effectively exploit historical data about navigation performance to modify the models in the planner, without any prior information oncerning the phenomenon being modeled. The plans produced by the adapted CL fail more rarely than the ones generated by a non-adaptive planner. The distributed learning system handles the new learning task autonomously, and is able to automatically identify the sensorial information most relevant for the task, thus reducing the communication and computational overhead of the predictive task},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_159900,

title = {An Experimental Evaluation of Reservoir Computation for Ambient Assisted Living},

author = {Bacciu Davide and CHESSA Stefano and Gallicchio Claudio and MICHELI Alessio and Barsocchi Paolo},

doi = {10.1007/978-3-642-35467-0_5},

year  = {2013},

date = {2013-01-01},

booktitle = {Neural Nets and Surroundings - 22nd Italian Workshop on Neural Nets},

journal = {SMART INNOVATION, SYSTEMS AND TECHNOLOGIES},

volume = {19},

pages = {41--50},

publisher = {Springer},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_193770,

title = {A General Purpose Distributed Learning Model for Robotic Ecologies},

author = {Bacciu Davide and Chessa Stefano and Gallicchio Claudio and Lenzi Alessandro and Micheli Alessio and Pelagatti Susanna},

url = {http://www.ifac-papersonline.net/Detailed/55807.html},

doi = {10.3182/20120905-3-HR-2030.00178},

year  = {2012},

date = {2012-01-01},

booktitle = {Robot Control - 10th IFAC Symposium on Robot Control},

journal = {IFAC PROCEEDINGS VOLUMES},

volume = {10},

pages = {435--440},

publisher = {ELSEVIER SCIENCE BV},

abstract = {The design of a learning system for robotic ecologies need to account for some key aspects of the ecology model such as distributivity, heterogeneity of the computational, sensory and actuator capabilities, as well as self-configurability. The paper proposes general guiding principles for learning systems' design that ensue from key ecology properties, and presents a distributed learning system for the Rubicon ecology that draws inspiration from such guidelines. The proposed learning system provides the Rubicon ecology with a set of general-purpose learning services which can be used to learn generic computational tasks that involve predicting information of interest based on dynamic sensorial input streams.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466867,

title = {Self-Sustaining Learning for Robotic Ecologies},

author = {BACCIU Davide and Broxvall Mathias and Coleman Sonya and Dragone Mauro and Gallicchio Claudio and Gennaro Claudio and Guzman Roberto and Lopez Raul and Lozano-Peiteado Hector and Ray AK and Renteria Arantxa and Saffiotti Alessandro and Vairo Claudio},

year  = {2012},

date = {2012-01-01},

booktitle = {Proceedings of the 1st International Conference on Sensor Networks, SENSORNETS 2012},

pages = {99--103},

abstract = {The most common use of wireless sensor networks (WSNs) is to collect environmental data from a specific area, and to channel it to a central processing node for on-line or off-line analysis. The WSN technology, however, can be used for much more ambitious goals. We claim that merging the concepts and technology of WSN with the concepts and technology of distributed robotics and multi-agent systems can open new ways to design systems able to provide intelligent services in our homes and working places. We also claim that endowing these systems with learning capabilities can greatly increase their viability and acceptability, by simplifying design, customization and adaptation to changing user needs. To support these claims, we illustrate our architecture for an adaptive robotic ecology, named RUBICON, consisting of a network of sensors, effectors and mobile robots.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_202140,

title = {Predicting User Movements in Heterogeneous Indoor Environments by Reservoir Computing},

author = {Bacciu Davide and Gallicchio Claudio and Micheli Alessio and Barsocchi Paolo and Chessa Stefano},

url = {http://ijcai-11.iiia.csic.es/files/proceedings/Space,%20Time%20and%20Ambient%20Intelligence%20Proceeding.pdf},

year  = {2011},

date = {2011-01-01},

urldate = {2011-01-01},

booktitle = {Proceedings of the IJCAI Workshop on Space, Time and Ambient Intelligence (STAMI)},

pages = {1--6},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466670,

title = {A Robust Bio-Inspired Clustering Algorithm for the Automatic Determination of Unknown Cluster Number},

author = {Bacciu Davide and Starita Antonina },

doi = {10.1109/IJCNN.2007.4371148},

year  = {2007},

date = {2007-01-01},

urldate = {2007-01-01},

booktitle = {2007 International Joint Conference on Neural Networks},

pages = {1314--1319},

publisher = {IEEE},

abstract = {The paper introduces a robust clustering algorithm that can automatically determine the unknown cluster number from noisy data without any a-priori information. We show how our clustering algorithm can be derived from a general learning theory, named CoRe learning, that models a cortical memory mechanism called repetition suppression. Moreover, we describe CoRe clustering relationships with Rival Penalized Competitive Learning (RPCL), showing how CoRe extends this model by strengthening the rival penalization estimation by means of robust loss functions. Finally, we present the results of simulations concerning the unsupervised segmentation of noisy images.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466676,

title = {Competitive Repetition-suppression (CoRe) Learning},

author = {Bacciu Davide and Starita Antonina },

doi = {10.1007/11840817_14},

year  = {2006},

date = {2006-01-01},

urldate = {2006-01-01},

booktitle = {ARTIFICIAL NEURAL NETWORKS - ICANN 2006, PT 1, Lecture Notes in Computer Science},

journal = {LECTURE NOTES IN COMPUTER SCIENCE},

volume = {4131},

pages = {130--139},

publisher = {Springer Verlag},

abstract = {The paper introduces Competitive Repetition-suppression (CoRe) learning, a novel paradigm inspired by a cortical mechanism of perceptual learning called repetition suppression. CoRe learning is an unsupervised, soft-competitive [1] model with conscience [2] that can be used for self-generating compact neural representations of the input stimuli. The key idea underlying the development of CoRe learning is to exploit the temporal distribution of neurons activations as a source of training information and to drive memory formation. As a case study, the paper reports the CoRe learning rules that have been derived for the unsupervised training of a Radial Basis Function network.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}

@conference{11568_466876,

title = {A RLWPR network for learning the internal model of an anthropomorphic robot arm},

author = {Bacciu Davide and Zollo Loredana and Guglielmelli Eugenio and Leoni Fabio and Starita Antonina},

doi = {10.1109/IROS.2004.1389362},

year  = {2004},

date = {2004-01-01},

urldate = {2004-01-01},

booktitle = {Proceedings of the 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},

volume = {1},

pages = {260--265},

publisher = {IEEE},

abstract = {Studies of human motor control suggest that humans develop internal models of the arm during the execution of voluntary movements. In particular, the internal model consists of the inverse dynamic model of the muscolo-skeletal system and intervenes in the feedforward loop of the motor control system to improve reactivity and stability in rapid movements. In this paper, an interaction control scheme inspired by biological motor control is resumed, i.e. the coactivation-based compliance control in the joint space and a feedforward module capable of online learning the manipulator inverse dynamics is presented. A novel recurrent learning paradigm is proposed which derives from an interesting functional equivalence between locally weighted regression networks and lakagi-Sugeno-Kang fuzzy systems. The proposed learning paradigm has been named recurrent locally weighted regression networks and strengthens the computational power of feedforward locally weighted regression networks. Simulation results are reported to validate the control scheme.},

keywords = {},

pubstate = {published},

tppubtype = {conference}

}