AHCI RESEARCH GROUP
Publications
Papers published in international journals,
proceedings of conferences, workshops and books.
OUR RESEARCH
Scientific Publications
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2020
Trifir`o, Irene; Augello, Agnese; Maniscalco, Umberto; Pilato, Giovanni; Vella, Filippo; Meo, Rosa
How Are You? How a Robot Can Learn to Express Its Own Roboceptions Proceedings Article
In: Cristiani, Matteo; Toro, Carlos; Zanni-Merk, Cecilia; Howlett, Robert J.; Jain, Lakhmi C. (Ed.): Procedia Computer Science, pp. 480–489, Elsevier B.V., 2020.
Abstract | Links | BibTeX | Tags: Human computer interaction, Knowledge Representation, Latent Semantic Analysis, Natural Language Processing, Robotics, Semantic Computing, Social Robots
@inproceedings{trifiroHowAreYou2020,
title = {How Are You? How a Robot Can Learn to Express Its Own Roboceptions},
author = { Irene Trifir{`o} and Agnese Augello and Umberto Maniscalco and Giovanni Pilato and Filippo Vella and Rosa Meo},
editor = { Matteo Cristiani and Carlos Toro and Cecilia {Zanni-Merk} and Robert J. Howlett and Lakhmi C. Jain},
doi = {10.1016/j.procs.2020.08.050},
year = {2020},
date = {2020-01-01},
booktitle = {Procedia Computer Science},
volume = {176},
pages = {480--489},
publisher = {Elsevier B.V.},
abstract = {This work is framed on investigating how a robot can learn associations between linguistic elements, such as words or sentences, and its bodily perceptions, that we named ``roboceptions''. We discuss the possibility of defining such a process of an association through the interaction with human beings. By interacting with a user, the robot can learn to ascribe a meaning to its roboceptions to express them in natural language. Such a process could then be used by the robot in a verbal interaction to detect some words recalling the previously experimented roboceptions. In this paper, we discuss a Dual-NMT approach to realize such an association. However, it requires adequate training corpus. For this reason, we consider two different phases towards the realization of the system, and we show the results of the first phase, comparing two approaches: one based on the Latent Semantic Analysis paradigm and one based on the Random Indexing methodology.},
keywords = {Human computer interaction, Knowledge Representation, Latent Semantic Analysis, Natural Language Processing, Robotics, Semantic Computing, Social Robots},
pubstate = {published},
tppubtype = {inproceedings}
}
This work is framed on investigating how a robot can learn associations between linguistic elements, such as words or sentences, and its bodily perceptions, that we named ``roboceptions''. We discuss the possibility of defining such a process of an association through the interaction with human beings. By interacting with a user, the robot can learn to ascribe a meaning to its roboceptions to express them in natural language. Such a process could then be used by the robot in a verbal interaction to detect some words recalling the previously experimented roboceptions. In this paper, we discuss a Dual-NMT approach to realize such an association. However, it requires adequate training corpus. For this reason, we consider two different phases towards the realization of the system, and we show the results of the first phase, comparing two approaches: one based on the Latent Semantic Analysis paradigm and one based on the Random Indexing methodology.
2015
Ditta, Marilena; Milazzo, Fabrizio; Raví, Valentina; Pilato, Giovanni; Augello, Agnese
Data-Driven Relation Discovery from Unstructured Texts Proceedings Article
In: A., Filipe J. Liu K. Aveiro D. Dietz J. Filipe J. Fred (Ed.): IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp. 597–602, SciTePress, 2015, ISBN: 978-989-758-158-8.
Abstract | BibTeX | Tags: Knowledge Management, Knowledge Representation, Latent Semantic Analysis, Semantic Computing
@inproceedings{dittaDatadrivenRelationDiscovery2015,
title = {Data-Driven Relation Discovery from Unstructured Texts},
author = { Marilena Ditta and Fabrizio Milazzo and Valentina Raví and Giovanni Pilato and Agnese Augello},
editor = { Filipe J. Liu K. Aveiro D. Dietz J. Filipe J. Fred A.},
isbn = {978-989-758-158-8},
year = {2015},
date = {2015-01-01},
booktitle = {IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management},
volume = {1},
pages = {597--602},
publisher = {SciTePress},
abstract = {This work proposes a data driven methodology for the extraction of subject-verb-object triplets from a text corpus. Previous works on the field solved the problem by means of complex learning algorithms requiring hand-crafted examples; our proposal completely avoids learning triplets from a dataset and is built on top of a well-known baseline algorithm designed by Delia Rusu et al.. The baseline algorithm uses only syntactic information for generating triplets and is characterized by a very low precision i.e., very few triplets are meaningful. Our idea is to integrate the semantics of the words with the aim of filtering out the wrong triplets, thus increasing the overall precision of the system. The algorithm has been tested over the Reuters Corpus and has it as shown good performance with respect to the baseline algorithm for triplet extraction. textcopyright 2015 by SCITEPRESS - Science and Technology Publications, Lda.},
keywords = {Knowledge Management, Knowledge Representation, Latent Semantic Analysis, Semantic Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
This work proposes a data driven methodology for the extraction of subject-verb-object triplets from a text corpus. Previous works on the field solved the problem by means of complex learning algorithms requiring hand-crafted examples; our proposal completely avoids learning triplets from a dataset and is built on top of a well-known baseline algorithm designed by Delia Rusu et al.. The baseline algorithm uses only syntactic information for generating triplets and is characterized by a very low precision i.e., very few triplets are meaningful. Our idea is to integrate the semantics of the words with the aim of filtering out the wrong triplets, thus increasing the overall precision of the system. The algorithm has been tested over the Reuters Corpus and has it as shown good performance with respect to the baseline algorithm for triplet extraction. textcopyright 2015 by SCITEPRESS - Science and Technology Publications, Lda.
Spiccia, Carmelo; Augello, Agnese; Pilato, Giovanni
Posgram Driven Word Prediction Proceedings Article
In: A., Dietz J. Aveiro D. Liu K. Filipe J. Filipe J. Fred (Ed.): IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, pp. 589–596, SciTePress, 2015, ISBN: 978-989-758-158-8.
Abstract | Links | BibTeX | Tags: Knowledge Management, Knowledge Representation, Semantic Computing
@inproceedings{spicciaPosgramDrivenWord2015,
title = {Posgram Driven Word Prediction},
author = { Carmelo Spiccia and Agnese Augello and Giovanni Pilato},
editor = { Dietz J. Aveiro D. Liu K. Filipe J. Filipe J. Fred A.},
doi = {10.5220/0005613305890596},
isbn = {978-989-758-158-8},
year = {2015},
date = {2015-01-01},
booktitle = {IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management},
volume = {1},
pages = {589--596},
publisher = {SciTePress},
abstract = {Several word prediction algorithms have been described in literature for automatic sentence completion from a finite candidate words set. However, at the best of our knowledge, very little or no work has been done on reducing the cardinality of this set. To address this issue, we use posgrams to predict the part of speech of the missing word first. Candidate words are then restricted to the ones fulfilling the predicted part of speech. We show how this additional step can improve the processing speed and the accuracy of word predictors. Experimental results are provided for the Italian language. textcopyright 2015 by SCITEPRESS - Science and Technology Publications, Lda.},
keywords = {Knowledge Management, Knowledge Representation, Semantic Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
Several word prediction algorithms have been described in literature for automatic sentence completion from a finite candidate words set. However, at the best of our knowledge, very little or no work has been done on reducing the cardinality of this set. To address this issue, we use posgrams to predict the part of speech of the missing word first. Candidate words are then restricted to the ones fulfilling the predicted part of speech. We show how this additional step can improve the processing speed and the accuracy of word predictors. Experimental results are provided for the Italian language. textcopyright 2015 by SCITEPRESS - Science and Technology Publications, Lda.
2014
Augello, Agnese; Pilato, Giovanni; Vassallo, Giorgio; Gaglio, Salvatore
Chatbots as Interface to Ontologies Journal Article
In: Advances in Intelligent Systems and Computing, vol. 260, pp. 285–299, 2014, ISSN: 21945357.
Abstract | Links | BibTeX | Tags: Chatbots, Conversational Agents, Knowledge Representation, Natural Language Processing, Ontologies
@article{augelloChatbotsInterfaceOntologies2014,
title = {Chatbots as Interface to Ontologies},
author = { Agnese Augello and Giovanni Pilato and Giorgio Vassallo and Salvatore Gaglio},
doi = {10.1007/978-3-319-03992-3_20},
issn = {21945357},
year = {2014},
date = {2014-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {260},
pages = {285--299},
abstract = {Chatbots are simple conversational agents using 'pattern matching rules' to carry out the dialogue with the user and various expedients to improve their credibility. However, the rules on which they are based on are too restrictive and their language understanding capability is very limited. Nevertheless chatbots are widespread in several applications, especially to provide information to users in a new and enjoyable way. In this chapter we describe different chatbot architectures, exploiting the use of ontologies in order to create clever information suppliers overcoming the main limits of chatbots: The knowledge base building and the rigidness of the dialogue mechanism. textcopyright Springer International Publishing Switzerland 2014.},
keywords = {Chatbots, Conversational Agents, Knowledge Representation, Natural Language Processing, Ontologies},
pubstate = {published},
tppubtype = {article}
}
Chatbots are simple conversational agents using 'pattern matching rules' to carry out the dialogue with the user and various expedients to improve their credibility. However, the rules on which they are based on are too restrictive and their language understanding capability is very limited. Nevertheless chatbots are widespread in several applications, especially to provide information to users in a new and enjoyable way. In this chapter we describe different chatbot architectures, exploiting the use of ontologies in order to create clever information suppliers overcoming the main limits of chatbots: The knowledge base building and the rigidness of the dialogue mechanism. textcopyright Springer International Publishing Switzerland 2014.
2013
Augello, Agnese; Gaglio, Salvatore; Oliveri, Gianluigi; Pilato, Giovanni
Acting on Conceptual Spaces in Cognitive Agents Proceedings Article
In: M., Lieto A. Cruciani (Ed.): CEUR Workshop Proceedings, pp. 25–32, CEUR-WS, 2013.
Abstract | BibTeX | Tags: Artificial intelligence, Cognitive Systems, Conceptual Spaces, Knowledge Representation
@inproceedings{augelloActingConceptualSpaces2013,
title = {Acting on Conceptual Spaces in Cognitive Agents},
author = { Agnese Augello and Salvatore Gaglio and Gianluigi Oliveri and Giovanni Pilato},
editor = { Lieto A. Cruciani M.},
year = {2013},
date = {2013-01-01},
booktitle = {CEUR Workshop Proceedings},
volume = {1100},
pages = {25--32},
publisher = {CEUR-WS},
abstract = {Conceptual spaces were originally introduced by Gärdenfors as a bridge between symbolic and connectionist models of information representation. In our opinion, a cognitive agent, besides being able to work within his (current) conceptual space, must also be able to 'produce a new space' by means of 'global' operations. These are operations which, acting on a conceptual space taken as a whole, generate other conceptual spaces. Copyright textcopyright 2013 for the individual papers by the papers' authors.},
keywords = {Artificial intelligence, Cognitive Systems, Conceptual Spaces, Knowledge Representation},
pubstate = {published},
tppubtype = {inproceedings}
}
Conceptual spaces were originally introduced by Gärdenfors as a bridge between symbolic and connectionist models of information representation. In our opinion, a cognitive agent, besides being able to work within his (current) conceptual space, must also be able to 'produce a new space' by means of 'global' operations. These are operations which, acting on a conceptual space taken as a whole, generate other conceptual spaces. Copyright textcopyright 2013 for the individual papers by the papers' authors.
Augello, Agnese; Gaglio, Salvatore; Pilato, Giovanni; Vassallo, Giorgio
Clifford Rotors for Conceptual Representation in Chatbots Journal Article
In: Advances in Intelligent Systems and Computing, vol. 196 AISC, pp. 369–370, 2013, ISSN: 21945357.
Abstract | Links | BibTeX | Tags: Chatbots, Clifford algebra, Conceptual Spaces, Geometric algebra, Knowledge Representation, Latent Semantic Analysis, Natural Language Processing, Semantic Computing
@article{augelloCliffordRotorsConceptual2013,
title = {Clifford Rotors for Conceptual Representation in Chatbots},
author = { Agnese Augello and Salvatore Gaglio and Giovanni Pilato and Giorgio Vassallo},
doi = {10.1007/978-3-642-34274-5_64},
issn = {21945357},
year = {2013},
date = {2013-01-01},
journal = {Advances in Intelligent Systems and Computing},
volume = {196 AISC},
pages = {369--370},
abstract = {In this abstract we introduce an unsupervised sub-symbolic natural language sentences encoding procedure aimed at catching and representing into a Chatbot Knowledge Base (KB) the concepts expressed by an user interacting with a robot. The chatbot KB is coded in a conceptual space induced from the application of the Latent Semantic Analysis (LSA) paradigm on a corpus of documents. LSA has the effect of decomposing the original relationships between elements into linearly-independent vectors. Each basis vector can be considered therefore as a "conceptual coordinate", which can be tagged by the words which better characterize it. This tagging is obtained by performing a (TF-IDF)-like weighting schema [3], that we call TW-ICW (term weight-inverse conceptual coordinate weight), to weigh the relevance of each term on each conceptual coordinate. textcopyright 2013 Springer-Verlag.},
keywords = {Chatbots, Clifford algebra, Conceptual Spaces, Geometric algebra, Knowledge Representation, Latent Semantic Analysis, Natural Language Processing, Semantic Computing},
pubstate = {published},
tppubtype = {article}
}
In this abstract we introduce an unsupervised sub-symbolic natural language sentences encoding procedure aimed at catching and representing into a Chatbot Knowledge Base (KB) the concepts expressed by an user interacting with a robot. The chatbot KB is coded in a conceptual space induced from the application of the Latent Semantic Analysis (LSA) paradigm on a corpus of documents. LSA has the effect of decomposing the original relationships between elements into linearly-independent vectors. Each basis vector can be considered therefore as a "conceptual coordinate", which can be tagged by the words which better characterize it. This tagging is obtained by performing a (TF-IDF)-like weighting schema [3], that we call TW-ICW (term weight-inverse conceptual coordinate weight), to weigh the relevance of each term on each conceptual coordinate. textcopyright 2013 Springer-Verlag.
2012
Augello, Agnese; Gentile, Manuel; Pilato, Giovanni; Vassallo, Giorgio
Geometric Encoding of Sentences Based on Clifford Algebra Proceedings Article
In: KDIR 2012 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval, pp. 457–462, 2012, ISBN: 978-989-8565-29-7.
Abstract | BibTeX | Tags: Geometric algebra, Information Retrieval, Knowledge Representation, Natural Language Processing, Semantic Computing, Semantic Spaces
@inproceedings{augelloGeometricEncodingSentences2012,
title = {Geometric Encoding of Sentences Based on Clifford Algebra},
author = { Agnese Augello and Manuel Gentile and Giovanni Pilato and Giorgio Vassallo},
isbn = {978-989-8565-29-7},
year = {2012},
date = {2012-01-01},
booktitle = {KDIR 2012 - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval},
pages = {457--462},
abstract = {Natural language sentences can be represented as vectors in a high dimensional vector space. Generally, these models are based on bag of words approaches, and therefore they do not fully capture the semantics of sentences which depends both by the semantics of the words, and their order in in the phrase. In this work we propose a sub-symbolic methodology to encode natural language sentences considering both these two aspects. The proposed approach exploits the properties of Geometric Algebra rotation operators, called rotors, to code sentences through the rotation of an orthogonal basis of a semantic space. The methodology is based on three main steps: the construction of a semantic space, the association of ad-hoc rotors to sentence bigrams, and finally the coding of the sentence through the application of the obtained rotors to a standard basis in the semantic space. Copyright textcopyright 2012 SciTePress - Science and Technology Publications.},
keywords = {Geometric algebra, Information Retrieval, Knowledge Representation, Natural Language Processing, Semantic Computing, Semantic Spaces},
pubstate = {published},
tppubtype = {inproceedings}
}
Natural language sentences can be represented as vectors in a high dimensional vector space. Generally, these models are based on bag of words approaches, and therefore they do not fully capture the semantics of sentences which depends both by the semantics of the words, and their order in in the phrase. In this work we propose a sub-symbolic methodology to encode natural language sentences considering both these two aspects. The proposed approach exploits the properties of Geometric Algebra rotation operators, called rotors, to code sentences through the rotation of an orthogonal basis of a semantic space. The methodology is based on three main steps: the construction of a semantic space, the association of ad-hoc rotors to sentence bigrams, and finally the coding of the sentence through the application of the obtained rotors to a standard basis in the semantic space. Copyright textcopyright 2012 SciTePress - Science and Technology Publications.
2011
Augello, Agnese; Scriminaci, Mario; Gaglio, Salvatore; Pilato, Giovanni
A Modular Framework for Versatile Conversational Agent Building Proceedings Article
In: Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011, pp. 577–582, 2011, ISBN: 978-0-7695-4373-4.
Abstract | Links | BibTeX | Tags: Conversational Agents, Intelligent Agents, Knowledge Representation, Ontologies, Semantic Computing, Semantic Spaces
@inproceedings{augelloModularFrameworkVersatile2011,
title = {A Modular Framework for Versatile Conversational Agent Building},
author = { Agnese Augello and Mario Scriminaci and Salvatore Gaglio and Giovanni Pilato},
doi = {10.1109/CISIS.2011.95},
isbn = {978-0-7695-4373-4},
year = {2011},
date = {2011-01-01},
booktitle = {Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2011},
pages = {577--582},
abstract = {This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible. textcopyright 2011 IEEE.},
keywords = {Conversational Agents, Intelligent Agents, Knowledge Representation, Ontologies, Semantic Computing, Semantic Spaces},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper illustrates a web-based infrastructure of an architecture for conversational agents equipped with a modular knowledge base. This solution has the advantage to allow the building of specific modules that deal with particular features of a conversation (ranging from its topic to the manner of reasoning of the chatbot). This enhances the agent interaction capabilities. The approach simplifies the chatbot knowledge base design process: extending, generalizing or even restricting the chatbot knowledge base in order to suit it to manage specific dialoguing tasks as much as possible. textcopyright 2011 IEEE.
Greco, Luca; Presti, Liliana Lo; Augello, Agnese; Re, Giuseppe Lo; Cascia, Marco La; Gaglio, Salvatore
A Multi-Agent Decision Support System for Dynamic Supply Chain Organization Proceedings Article
In: CEUR Workshop Proceedings, CEUR-WS, 2011.
Abstract | BibTeX | Tags: Decision Networks, Decision Support Systems, Dynamic Supply Chains, Information Filtering, Knowledge Representation, Multi-agent systems
@inproceedings{grecoMultiagentDecisionSupport2011,
title = {A Multi-Agent Decision Support System for Dynamic Supply Chain Organization},
author = { Luca Greco and Liliana Lo Presti and Agnese Augello and Giuseppe Lo Re and Marco La Cascia and Salvatore Gaglio},
year = {2011},
date = {2011-01-01},
booktitle = {CEUR Workshop Proceedings},
volume = {771},
publisher = {CEUR-WS},
abstract = {In this work, a multi-agent system (MAS) for supply chain dynamic configuration is proposed. The brain of each agent is composed of a Bayesian Decision Network (BDN); this choice allows the agent for taking the best decisions estimating benefits and potential risks of different strategies, analyzing and managing uncertain information about the collaborating companies. Each agent collects information about customer's orders and current market prices, and analyzes previous experiences of collaborations with trading partners. The agent therefore performs a probabilistic inferential reasoning to filter information modeled in its knowledge base in order to achieve the best performance in the supply chain organization.},
keywords = {Decision Networks, Decision Support Systems, Dynamic Supply Chains, Information Filtering, Knowledge Representation, Multi-agent systems},
pubstate = {published},
tppubtype = {inproceedings}
}
In this work, a multi-agent system (MAS) for supply chain dynamic configuration is proposed. The brain of each agent is composed of a Bayesian Decision Network (BDN); this choice allows the agent for taking the best decisions estimating benefits and potential risks of different strategies, analyzing and managing uncertain information about the collaborating companies. Each agent collects information about customer's orders and current market prices, and analyzes previous experiences of collaborations with trading partners. The agent therefore performs a probabilistic inferential reasoning to filter information modeled in its knowledge base in order to achieve the best performance in the supply chain organization.
Pilato, Giovanni; Augello, Agnese; Gaglio, Salvatore
A Modular Architecture for Adaptive ChatBots Proceedings Article
In: Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011, pp. 177–180, 2011, ISBN: 978-0-7695-4492-2.
Abstract | Links | BibTeX | Tags: Chatbots, Conversational Agents, Intelligent Agents, Knowledge Representation, Semantic Computing
@inproceedings{pilatoModularArchitectureAdaptive2011,
title = {A Modular Architecture for Adaptive ChatBots},
author = { Giovanni Pilato and Agnese Augello and Salvatore Gaglio},
doi = {10.1109/ICSC.2011.68},
isbn = {978-0-7695-4492-2},
year = {2011},
date = {2011-01-01},
booktitle = {Proceedings - 5th IEEE International Conference on Semantic Computing, ICSC 2011},
pages = {177--180},
abstract = {We illustrate an architecture for a conversational agent based on a modular knowledge representation. This solution provides intelligent conversational agents with a dynamic and flexible behavior. The modularity of the architecture allows a concurrent and synergic use of different techniques, making it possible to use the most adequate methodology for the management of a specific characteristic of the domain, of the dialogue, or of the user behavior. We show the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation techniques and capable to differently manage conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, whose task is to choose, time by time, the most adequate chatbot knowledge section to activate. textcopyright 2011 IEEE.},
keywords = {Chatbots, Conversational Agents, Intelligent Agents, Knowledge Representation, Semantic Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
We illustrate an architecture for a conversational agent based on a modular knowledge representation. This solution provides intelligent conversational agents with a dynamic and flexible behavior. The modularity of the architecture allows a concurrent and synergic use of different techniques, making it possible to use the most adequate methodology for the management of a specific characteristic of the domain, of the dialogue, or of the user behavior. We show the implementation of a proof-of-concept prototype: a set of modules exploiting different knowledge representation techniques and capable to differently manage conversation features has been developed. Each module is automatically triggered through a component, named corpus callosum, whose task is to choose, time by time, the most adequate chatbot knowledge section to activate. textcopyright 2011 IEEE.
Pilato, Giovanni; Augello, Agnese; Gaglio, Salvatore
Modular Knowledge Representation in Advisor Agents for Situation Awareness Journal Article
In: International Journal of Semantic Computing, vol. 5, no. 1, pp. 33–53, 2011, ISSN: 1793351X.
Abstract | Links | BibTeX | Tags: Chatbots, Context awareness, Conversational Agents, Decision Support Systems, Knowledge Representation, Semantic Computing
@article{pilatoModularKnowledgeRepresentation2011,
title = {Modular Knowledge Representation in Advisor Agents for Situation Awareness},
author = { Giovanni Pilato and Agnese Augello and Salvatore Gaglio},
doi = {10.1142/S1793351X11001158},
issn = {1793351X},
year = {2011},
date = {2011-01-01},
journal = {International Journal of Semantic Computing},
volume = {5},
number = {1},
pages = {33--53},
abstract = {A modular knowledge representation framework for conversational agents is presented. The approach has been realized to suit the situation awareness paradigm. The modularity of the framework makes possible the composition of specific modules that deal with particular features, simplifying both the chatbot design process and its smartness. As a proof of concepts we have developed a modular, situation awareness oriented, KB for a conversational agent, which plays the role of an advisor aimed at helping a user to be in charge of a virtual town, inspired to the SimCity series game. The agent makes an extensive use of semantic computing techniques and is able to perceive, comprehend and project consequences of actions in order to handle strategic decision under uncertainty conditions. textcopyright 2011 World Scientific Publishing Company.},
keywords = {Chatbots, Context awareness, Conversational Agents, Decision Support Systems, Knowledge Representation, Semantic Computing},
pubstate = {published},
tppubtype = {article}
}
A modular knowledge representation framework for conversational agents is presented. The approach has been realized to suit the situation awareness paradigm. The modularity of the framework makes possible the composition of specific modules that deal with particular features, simplifying both the chatbot design process and its smartness. As a proof of concepts we have developed a modular, situation awareness oriented, KB for a conversational agent, which plays the role of an advisor aimed at helping a user to be in charge of a virtual town, inspired to the SimCity series game. The agent makes an extensive use of semantic computing techniques and is able to perceive, comprehend and project consequences of actions in order to handle strategic decision under uncertainty conditions. textcopyright 2011 World Scientific Publishing Company.
Ribino, Patrizia; Augello, Agnese; Re, Giuseppe Lo; Gaglio, Salvatore
A Knowledge Management and Decision Support Model for Enterprises Journal Article
In: Advances in Decision Sciences, vol. 2011, 2011, ISSN: 20903359.
Abstract | Links | BibTeX | Tags: Decision Support Systems, Knowledge Representation
@article{ribinoKnowledgeManagementDecision2011,
title = {A Knowledge Management and Decision Support Model for Enterprises},
author = { Patrizia Ribino and Agnese Augello and Giuseppe Lo Re and Salvatore Gaglio},
doi = {10.1155/2011/425820},
issn = {20903359},
year = {2011},
date = {2011-01-01},
journal = {Advances in Decision Sciences},
volume = {2011},
abstract = {We propose a novel knowledge management system (KMS) for enterprises. Our system exploits two different approaches for knowledge representation and reasoning: a document-based approach based on data-driven creation of a semantic space and an ontology-based model. Furthermore, we provide an expert system capable of supporting the enterprise decisional processes and a semantic engine which performs intelligent search on the enterprise knowledge bases. The decision support process exploits the Bayesian networks model to improve business planning process when performed under uncertainty. Copyright textcopyright 2011 Patrizia Ribino et al.},
keywords = {Decision Support Systems, Knowledge Representation},
pubstate = {published},
tppubtype = {article}
}
We propose a novel knowledge management system (KMS) for enterprises. Our system exploits two different approaches for knowledge representation and reasoning: a document-based approach based on data-driven creation of a semantic space and an ontology-based model. Furthermore, we provide an expert system capable of supporting the enterprise decisional processes and a semantic engine which performs intelligent search on the enterprise knowledge bases. The decision support process exploits the Bayesian networks model to improve business planning process when performed under uncertainty. Copyright textcopyright 2011 Patrizia Ribino et al.
2010
Augello, Agnese; Pilato, Giovanni; Gaglio, Salvatore
An Intelligent Advisor to Suggest Strategies in Economic Policy Decisions Proceedings Article
In: CISIS 2010 - The 4th International Conference on Complex, Intelligent and Software Intensive Systems, pp. 734–739, 2010, ISBN: 978-0-7695-3967-6.
Abstract | Links | BibTeX | Tags: Artificial intelligence, Conversational Agents, Decision Networks, Decision Support Systems, Knowledge Representation, Ontologies
@inproceedings{augelloIntelligentAdvisorSuggest2010,
title = {An Intelligent Advisor to Suggest Strategies in Economic Policy Decisions},
author = { Agnese Augello and Giovanni Pilato and Salvatore Gaglio},
doi = {10.1109/CISIS.2010.75},
isbn = {978-0-7695-3967-6},
year = {2010},
date = {2010-01-01},
booktitle = {CISIS 2010 - The 4th International Conference on Complex, Intelligent and Software Intensive Systems},
pages = {734--739},
abstract = {In this paper we illustrate the architecture of an agent that plays the role of an "intelligent advisor". The advisor is aimed at suggesting the best managing strategies for a model of a virtual town. A Decision Support System (DSS) embedded on a conversational agent constitutes the intelligent advisor architecture. Two interacting knowledge representation areas characterize the DSS. The first one deals with the description of the domain and the deterministic events through the use of ontologies. The second one deals with the management of situations characterized by uncertainty. The intelligent advisor tries to prospect the future evolutions of particular choices taken by the user, and as a consequence, suggests the player-user the best strategy given the current status of the game. textcopyright 2010 IEEE.},
keywords = {Artificial intelligence, Conversational Agents, Decision Networks, Decision Support Systems, Knowledge Representation, Ontologies},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper we illustrate the architecture of an agent that plays the role of an "intelligent advisor". The advisor is aimed at suggesting the best managing strategies for a model of a virtual town. A Decision Support System (DSS) embedded on a conversational agent constitutes the intelligent advisor architecture. Two interacting knowledge representation areas characterize the DSS. The first one deals with the description of the domain and the deterministic events through the use of ontologies. The second one deals with the management of situations characterized by uncertainty. The intelligent advisor tries to prospect the future evolutions of particular choices taken by the user, and as a consequence, suggests the player-user the best strategy given the current status of the game. textcopyright 2010 IEEE.
2009
Augello, Agnese; Vassallo, Giorgio; Gaglio, Salvatore; Pilato, Giovanni
A Semantic Layer on Semi-Structured Data Sources for Intuitive Chatbots Proceedings Article
In: Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2009, pp. 760–765, 2009, ISBN: 978-0-7695-3575-3.
Abstract | Links | BibTeX | Tags: Chatbots, Knowledge Representation, Natural Language Processing, Semantic Computing, Semantic Spaces
@inproceedings{augelloSemanticLayerSemistructured2009,
title = {A Semantic Layer on Semi-Structured Data Sources for Intuitive Chatbots},
author = { Agnese Augello and Giorgio Vassallo and Salvatore Gaglio and Giovanni Pilato},
doi = {10.1109/CISIS.2009.165},
isbn = {978-0-7695-3575-3},
year = {2009},
date = {2009-01-01},
booktitle = {Proceedings of the International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2009},
pages = {760--765},
abstract = {The main limits of chatbot technology are related to the building of their knowledge representation and to their rigid information retrieval and dialogue capabilities, usually based on simple "pattern matching rules". The analysis of distributional properties of words in a texts corpus allows the creation of semantic spaces where represent and compare natural language elements. This space can be interpreted as a "conceptual" space where the axes represent the latent primitive concepts of the analyzed corpus. The presented work aims at exploiting the properties of a data-driven semantic/conceptual space built using semistructured data sources freely available on the web, like Wikipedia. This coding is equivalent to adding, into the Wikipedia graph, a conceptual similarity relationship layer. The chatbot can exploit this layer in order to simulate an "intuitive" behavior, attempting to retrieve semantic relations between Wikipedia resources also through associative sub-symbolic paths. textcopyright 2009 IEEE.},
keywords = {Chatbots, Knowledge Representation, Natural Language Processing, Semantic Computing, Semantic Spaces},
pubstate = {published},
tppubtype = {inproceedings}
}
The main limits of chatbot technology are related to the building of their knowledge representation and to their rigid information retrieval and dialogue capabilities, usually based on simple "pattern matching rules". The analysis of distributional properties of words in a texts corpus allows the creation of semantic spaces where represent and compare natural language elements. This space can be interpreted as a "conceptual" space where the axes represent the latent primitive concepts of the analyzed corpus. The presented work aims at exploiting the properties of a data-driven semantic/conceptual space built using semistructured data sources freely available on the web, like Wikipedia. This coding is equivalent to adding, into the Wikipedia graph, a conceptual similarity relationship layer. The chatbot can exploit this layer in order to simulate an "intuitive" behavior, attempting to retrieve semantic relations between Wikipedia resources also through associative sub-symbolic paths. textcopyright 2009 IEEE.
2008
Pilato, Giovanni; Augello, Agnese; Vassallo, Giorgio; Gaglio, Salvatore
Sub-Symbolic Knowledge Representation for Evocative Chat-Bots Book
Physica-Verlag HD, 2008, ISBN: 978-3-7908-2009-6.
Abstract | Links | BibTeX | Tags: Chatbots, Knowledge Representation, Latent Semantic Analysis
@book{pilatoSubsymbolicKnowledgeRepresentation2008,
title = {Sub-Symbolic Knowledge Representation for Evocative Chat-Bots},
author = { Giovanni Pilato and Agnese Augello and Giorgio Vassallo and Salvatore Gaglio},
doi = {10.1007/978-3-7908-2010-2_42},
isbn = {978-3-7908-2009-6},
year = {2008},
date = {2008-01-01},
publisher = {Physica-Verlag HD},
abstract = {A sub-symbolic knowledge representation oriented to the enhancement of chat bot interaction is proposed. The result of the technique is the introduction of a semantic sub-symbolic layer to a traditional ontology-based knowledge representation. This layer is obtained mapping the ontology concepts into a semantic space built through Latent Semantic Analysis (LSA) technique and it is embedded into a conversational agent. This choice leads to a chat-bot with evocative capabilities whose knowledge representation framework is composed of two areas: the rational and the evocative one. As a standard ontology we have chosen the well-founded WordNet lexical dictionary, while as chat-bot the ALICE architecture. Experimental trials involving four lexical categories of WordNet have been conducted, and an example of interaction is shown at the end of the paper. textcopyright 2008 Physica-Verlag Heidelberg.},
keywords = {Chatbots, Knowledge Representation, Latent Semantic Analysis},
pubstate = {published},
tppubtype = {book}
}
A sub-symbolic knowledge representation oriented to the enhancement of chat bot interaction is proposed. The result of the technique is the introduction of a semantic sub-symbolic layer to a traditional ontology-based knowledge representation. This layer is obtained mapping the ontology concepts into a semantic space built through Latent Semantic Analysis (LSA) technique and it is embedded into a conversational agent. This choice leads to a chat-bot with evocative capabilities whose knowledge representation framework is composed of two areas: the rational and the evocative one. As a standard ontology we have chosen the well-founded WordNet lexical dictionary, while as chat-bot the ALICE architecture. Experimental trials involving four lexical categories of WordNet have been conducted, and an example of interaction is shown at the end of the paper. textcopyright 2008 Physica-Verlag Heidelberg.
2007
Pilato, Giovanni; Augello, Agnese; Vassallo, Giorgio; Gaglio, Salvatore
Sub-Symbolic Semantic Layer in Cyc for Intuitive Chat-Bots Proceedings Article
In: ICSC 2007 International Conference on Semantic Computing, pp. 121–128, 2007, ISBN: 0-7695-2997-6 978-0-7695-2997-4.
Abstract | Links | BibTeX | Tags: Artificial intelligence, Chatbots, Common Sense Reasoning, Conversational Agents, Cyc Ontology, Knowledge Representation, Latent Semantic Analysis, Ontologies, Semantic Computing, Semantic Spaces
@inproceedings{pilatoSubsymbolicSemanticLayer2007,
title = {Sub-Symbolic Semantic Layer in Cyc for Intuitive Chat-Bots},
author = { Giovanni Pilato and Agnese Augello and Giorgio Vassallo and Salvatore Gaglio},
doi = {10.1109/ICSC.2007.37},
isbn = {0-7695-2997-6 978-0-7695-2997-4},
year = {2007},
date = {2007-01-01},
booktitle = {ICSC 2007 International Conference on Semantic Computing},
pages = {121--128},
abstract = {The work presented in this paper aims to combine Latent Semantic Analysis methodology, common sense and traditional knowledge representation in order to improve the dialogue capabilities of a conversational agent. In our approach the agent brain is characterized by two areas: a "rational area", composed by a structured, rule-based knowledge base, and an "associative area", obtained through a data-driven semantic space. Concepts are mapped in this space and their mutual geometric distance is related to their conceptual similarity. The geometric distance between concepts implicitly defines a sub-symbolic relationship net, which can be seen as a new "subsymbolic semantic layer" automatically added to the Cyc ontology. Users queries can also be mapped in the same conceptual space, and evoke similar ontology concepts. As a result the agent can exploit this feature, attempting to retrieve ontological concepts that are not easily reachable by means of the traditional ontology reasoning engine. textcopyright 2007 IEEE.},
keywords = {Artificial intelligence, Chatbots, Common Sense Reasoning, Conversational Agents, Cyc Ontology, Knowledge Representation, Latent Semantic Analysis, Ontologies, Semantic Computing, Semantic Spaces},
pubstate = {published},
tppubtype = {inproceedings}
}
The work presented in this paper aims to combine Latent Semantic Analysis methodology, common sense and traditional knowledge representation in order to improve the dialogue capabilities of a conversational agent. In our approach the agent brain is characterized by two areas: a "rational area", composed by a structured, rule-based knowledge base, and an "associative area", obtained through a data-driven semantic space. Concepts are mapped in this space and their mutual geometric distance is related to their conceptual similarity. The geometric distance between concepts implicitly defines a sub-symbolic relationship net, which can be seen as a new "subsymbolic semantic layer" automatically added to the Cyc ontology. Users queries can also be mapped in the same conceptual space, and evoke similar ontology concepts. As a result the agent can exploit this feature, attempting to retrieve ontological concepts that are not easily reachable by means of the traditional ontology reasoning engine. textcopyright 2007 IEEE.
Santangelo, Antonella; Augello, Agnese; Sorce, Salvatore; Pilato, Giovanni; Gentile, Antonio; Genco, Alessandro; Gaglio, Salvatore
A Virtual Shopper Customer Assistant in Pervasive Environments Journal Article
In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4805 LNCS, no. PART 1, pp. 447–456, 2007, ISSN: 03029743.
Abstract | Links | BibTeX | Tags: Chatbots, Human computer interaction, Knowledge Representation, Multimodal Interaction, Pervasive Systems
@article{santangeloVirtualShopperCustomer2007,
title = {A Virtual Shopper Customer Assistant in Pervasive Environments},
author = { Antonella Santangelo and Agnese Augello and Salvatore Sorce and Giovanni Pilato and Antonio Gentile and Alessandro Genco and Salvatore Gaglio},
doi = {10.1007/978-3-540-76888-3_67},
issn = {03029743},
year = {2007},
date = {2007-01-01},
journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
volume = {4805 LNCS},
number = {PART 1},
pages = {447--456},
abstract = {In this work we propose a smart, human-like PDA-based personal shopper assistant. The system is able to understand the user needs through a spoken natural language interaction and then stores the preferences of the potential customer. Subsequently the personal shopper suggests the most suitable items and shops that match the user profile. The interaction is given by automatic speech recognition and text-to-speech technologies; localization is allowed by the use of Wireless technologies, while the interaction is performed by an Alice-based chat-bot endowed with reasoning capabilities. Besides, being implemented on a PDA, the personal shopper satisfies the user needs of mobility and it is also usable on different mobile devices. textcopyright Springer-Verlag Berlin Heidelberg 2007.},
keywords = {Chatbots, Human computer interaction, Knowledge Representation, Multimodal Interaction, Pervasive Systems},
pubstate = {published},
tppubtype = {article}
}
In this work we propose a smart, human-like PDA-based personal shopper assistant. The system is able to understand the user needs through a spoken natural language interaction and then stores the preferences of the potential customer. Subsequently the personal shopper suggests the most suitable items and shops that match the user profile. The interaction is given by automatic speech recognition and text-to-speech technologies; localization is allowed by the use of Wireless technologies, while the interaction is performed by an Alice-based chat-bot endowed with reasoning capabilities. Besides, being implemented on a PDA, the personal shopper satisfies the user needs of mobility and it is also usable on different mobile devices. textcopyright Springer-Verlag Berlin Heidelberg 2007.