Search Results for author:
Found 19 papers, 1 papers with code
On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)
Automated Planning and Scheduling is among the growing areas in Artificial Intelligence (AI) where mention of LLMs has gained popularity.
Legal Summarisation through LLMs: The PRODIGIT Project
We present some initial results of a large-scale Italian project called PRODIGIT which aims to support tax judges and lawyers through digital technology, focusing on AI.
Value-based Fast and Slow AI Nudging
Values that are relevant to a specific decision scenario are used to decide when and how to use each of these nudging modalities.
Understanding the Capabilities of Large Language Models for Automated Planning
Firstly, we want to understand the extent to which LLMs can be used for plan generation.
Fast and Slow Planning
The concept of Artificial Intelligence has gained a lot of attention over the last decade.
Plansformer: Generating Symbolic Plans using Transformers
Large Language Models (LLMs) have been the subject of active research, significantly advancing the field of Natural Language Processing (NLP).
Learning Behavioral Soft Constraints from Demonstrations
Many real-life scenarios require humans to make difficult trade-offs: do we always follow all the traffic rules or do we violate the speed limit in an emergency?
When Is It Acceptable to Break the Rules? Knowledge Representation of Moral Judgement Based on Empirical Data
We can invent novel rules on the fly.
Combining Fast and Slow Thinking for Human-like and Efficient Navigation in Constrained Environments
Current AI systems lack several important human capabilities, such as adaptability, generalizability, self-control, consistency, common sense, and causal reasoning.
Deep ensembles in bioimage segmentation
Semantic segmentation consists in classifying each pixel of an image by assigning it to a specific label chosen from a set of all the available ones.
Thinking Fast and Slow in AI: the Role of Metacognition
AI systems have seen dramatic advancement in recent years, bringing many applications that pervade our everyday life.
Making Human-Like Trade-offs in Constrained Environments by Learning from Demonstrations
To this end, we propose a novel inverse reinforcement learning (IRL) method for learning implicit hard and soft constraints from demonstrations, enabling agents to quickly adapt to new settings.
SenTag: a Web-based Tool for Semantic Annotation of Textual Documents
In this work, we present SenTag, a lightweight web-based tool focused on semantic annotation of textual documents.
The Effects of Air Quality on the Spread of the COVID-19 Pandemic in Italy: An Artificial Intelligence Approach
The COVID-19 pandemic considerably affects public health systems around the world.
Thinking Fast and Slow in AI
This paper proposes a research direction to advance AI which draws inspiration from cognitive theories of human decision making.
Modeling Contrary-to-Duty with CP-nets
In a ceteris-paribus semantics for deontic logic, a state of affairs where a larger set of prescriptions is respected is preferable to a state of affairs where some of them are violated.
Voting with Random Classifiers (VORACE): Theoretical and Experimental Analysis
In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources.
CPMetric: Deep Siamese Networks for Learning Distances Between Structured Preferences
CPDist is a novel metric learning approach based on the use of deep siamese networks which learn the Kendal Tau distance between partial orders that are induced by compact preference representations.
Logical Conditional Preference Theories
CP-nets represent the dominant existing framework for expressing qualitative conditional preferences between alternatives, and are used in a variety of areas including constraint solving.
