GEAR: A General Inference Engine for Automated MultiStrategy Reasoning

Ferilli, Stefano

doi:10.3390/electronics12020256

Cited by 6 publications

(3 citation statements)

References 49 publications

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“…In particular, our framework uses GEAR (an acronym for 'General Engine for Automated Reasoning') [72], a logical inference engine capable of MultiStrategy Reasoning, i.e., of integrating and bringing to cooperation several inference strategies in order to cope with the several complexities posed by real-world tasks and problems. The current prototype includes the following strategies: Deduction aims at making explicit knowledge that is implicit in the available knowledge but is a necessary consequence thereof.…”

Section: Automated Multistrategy Reasoning For Traffic Interpretationmentioning

confidence: 99%

An Interpretable and Explainable AI Framework for Urban-Suburban Traffic Analysis and Understanding

Ferilli¹,

Pierro²,

Redavid³

et al. 2023

Preprint

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Studying and understanding the behavior of people and vehicles on public roads can be of utmost importance for supporting the activities of many institutional stakeholders. It may allow automated supervision of the ongoing situation in a given place, with warnings or alarms raised in case of anomalies. It may be used to plan their interventions on road and town organization. It may provide them with advanced support to decision-making. The number of involved entities and places to manage makes it infeasible to manually handle all the traffic-related tasks. Moreover, the complexity of the tasks to be carried out requires the adoption of advanced approaches. Many AI solutions are nowadays mature to support these requirements. In some cases, the motivations and objectives of traffic management require the AI outcomes to be understandable, interpretable and explainable. In this paper, we propose TrAnSIT (TRaffic ANalysis Supervision and Interpretation Tool), an AI-based framework that combines several modules, each aimed at tackling a specific traffic-related task, so as to cover a wide landscape of traffic-related issues, from overall urban or suburban traffic management to surveying specific road segments that fall under the scope of one camera. Most of these modules are based on AI techniques that support a human-level understanding of the outcomes.

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Section: Automated Multistrategy Reasoning For Traffic Interpretationmentioning

confidence: 99%

An Interpretable and Explainable AI Framework for Urban-Suburban Traffic Analysis and Understanding

Ferilli¹,

Pierro²,

Redavid³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The Neo4j libraries for graph manipulation, querying, and mining; • Advanced MultiStrategy Reasoning tools that combine different inference strategies to tackle more complex problems and closely simulate human reasoning (e.g., as provided by GEAR [2]); • Standard SW representation techniques and reasoning tools to be interoperable with the existing SW world.…”

mentioning

confidence: 99%

“…Pattern Mining based on automated reasoning, which provides more expressive power than LPG-based query languages such as Cypher. For this, GraphBRAIN is connected to the GEAR [2] Multistrategy Reasoning engine, currently providing deduction, abduction, abstraction, induction, probabilistic, and similarity-based inferences.…”

mentioning

confidence: 99%

A Graph DB-Based Solution for Semantic Technologies in the Future Internet

Ferilli,

Bernasconi,

Di Pierro

et al. 2023

Future Internet

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With the progressive improvements in the power, effectiveness, and reliability of AI solutions, more and more critical human problems are being handled by automated AI-based tools and systems. For more complex or particularly critical applications, the level of knowledge, not just information, must be handled by systems where explicit relationships among objects are represented and processed. For this purpose, the knowledge representation branch of AI proposes Knowledge Graphs, widely used in the Semantic Web, where different online applications may interact by understanding the meaning of the data they process and exchange. This paper describes a framework and online platform for the Internet-based knowledge graph definition, population, and exploitation based on the LPG graph model. Its main advantages are its efficiency and representational power and the wide range of functions that it provides to its users beyond traditional Semantic Web reasoning: network analysis, data mining, multistrategy reasoning, and knowledge browsing. Still, it can also be mapped onto the SW.

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The GraphBRAIN Framework for Knowledge Graph Management and Its Applications to Cultural Heritage

Ferilli,

Bernasconi,

Di Pierro

et al. 2024

Lecture Notes in Computer Science

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The traditional record-based approach to the description of Cultural Heritage is nowadays obsolete. It is unable to properly handle complex descriptions and it cannot support advanced functions provided by Artificial Intelligence techniques for helping practitioners, scholars, researchers and end-users in carrying out their tasks. A graph-based, semantic approach is needed, such as that provided by Semantic Web solutions. Also, a ‘holistic’ description approach is needed, that includes and inter-connects all branches and types of Cultural Heritage, and that is not limited to describing just the formal metadata of cultural objects, but can deal with their content, physicality, context and lifecycle, as well. The GraphBRAIN framework and technology for Knowledge Graph management enforces all these ideas and enjoys improved efficiency, expressiveness, and flexibility thanks to the use of the LPG model for knowledge representation. This paper describes GraphBRAIN and its application to several Cultural Heritage-related fields, including digital libraries, archives and museums, history of computing, and tourism as a way to boost fruition of these items.

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GEAR: A General Inference Engine for Automated MultiStrategy Reasoning

Cited by 6 publications

References 49 publications

An Interpretable and Explainable AI Framework for Urban-Suburban Traffic Analysis and Understanding

An Interpretable and Explainable AI Framework for Urban-Suburban Traffic Analysis and Understanding

A Graph DB-Based Solution for Semantic Technologies in the Future Internet

The GraphBRAIN Framework for Knowledge Graph Management and Its Applications to Cultural Heritage

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