1,721,045 research outputs found
Towards explainable data-to-text generation
Full text linkIn recent years there has been a renewed burst of interest in systems able to textually summarize data, producing natural language text as a description of input data series. Many of the recently proposed approaches to solve the data-to-text task are based on Machine Learning (ML) and ultimately rely on Deep Learning (DL) techniques. This technological choice often prevents the system from enjoying explainability properties. In this paper we outline our ongoing research and present a framework that is ML/DL free and is conceived to be compliant with xAI requirements. In particular we design ASP/Python programs that enable explicit control of the abstraction process, descriptions' accuracy and relevance handling, and amount of synthesis. We provide a critical analysis of the xAI features that should be implemented and a working proof of concept that addresses crucial aspects in the abstraction of data. In particular we discuss: how to model and output the abstraction accuracy of a concept w.r.t. data; how to identify what to say with controlled synthesis level: i.e., the key descriptive elements to be addressed in the data; how to represent abstracted information by means of visual annotation to charts. The main advantages of such approach are a trustworthy and reliable description, a transparent methodology, logically provable output, and measured accuracy that can control natural language modulation of descriptions
Towards explainable data-to-text generation
No full textIn recent years there has been a renewed burst of interest in systems able to textually summarize data, producing natural language text as a description of input data series. Many of the recently proposed approaches to solve the data-to-text task are based on Machine Learning (ML) and ultimately rely on Deep Learning (DL) techniques. This technological choice often prevents the system from enjoying explainability properties. In this paper we outline our ongoing research and present a framework that is ML/DL free and is conceived to be compliant with xAI requirements. In particular we design ASP/Python programs that enable explicit control of the abstraction process, descriptions' accuracy and relevance handling, and amount of synthesis. We provide a critical analysis of the xAI features that should be implemented and a working proof of concept that addresses crucial aspects in the abstraction of data. In particular we discuss: how to model and output the abstraction accuracy of a concept w.r.t. data; how to identify what to say with controlled synthesis level: i.e., the key descriptive elements to be addressed in the data; how to represent abstracted information by means of visual annotation to charts. The main advantages of such approach are a trustworthy and reliable description, a transparent methodology, logically provable output, and measured accuracy that can control natural language modulation of descriptions
Modeling and Solving the Rush Hour puzzle
Full text linkWe introduce the physical puzzle Rush Hour and its generalization. We briefly survey its complexity limits, then we model and solve it using declarative paradigms. In particular, we provide a constraint programming encoding in MiniZinc and a model in Answer Set Programming and we report and compare experimental results. Although this is simply a game, the kind of reasoning involved is the same that autonomous vehicles should do for exiting a garage. This shows the potential of logic programming for problems concerning transport problems and self-driving cars
A Java visual simulator of turing machines
No full textWe present a graphical simulator of 1-Tape, k-Tapes, deterministic and non deterministic Turing machines. The simulator is written in Java and as such it runs on most platforms. During and after the computation it returns interesting features such as the amount of space visited/used and the number of steps. When simulating non-deterministic Turing machines it allows to browse the “tree” of non deterministic computations. It is developed purely for didactic purposes: it can be used in courses of Foundations of Computer Science, in courses of Computational Complexity, as well as in didactic projects with high schools
ECHO: A hierarchical combination of classical and multi-agent epistemic planning problems
Full text linkThe continuous interest in Artificial Intelligence (AI) has brought, among other things, the development of several scenarios where multiple artificial entities interact with each other. As for all the other autonomous settings, these multi-agent systems require orchestration. This is, generally, achieved through techniques derived from the vast field of Automated Planning. Notably, arbitration in multi-agent domains is not only tasked with regulating how the agents act, but must also consider the interactions between the agents' information flows and must, therefore, reason on an epistemic level. This brings a substantial overhead that often diminishes the reasoning process's usability in real-world situations. To address this problem, we present ECHO, a hierarchical framework that embeds classical and multi-agent epistemic (epistemic, for brevity) planners in a single architecture. The idea is to combine (i) classical; and(ii) epistemic solvers to model efficiently the agents' interactions with the (i) 'physical world'; and(ii) information flows, respectively. In particular, the presented architecture starts by planning on the 'epistemic level', with a high level of abstraction, focusing only on the information flows. Then it refines the planning process, due to the classical planner, to fully characterize the interactions with the 'physical' world. To further optimize the solving process, we introduced the concept of macros in epistemic planning and enriched the 'classical' part of the domain with goal-networks. Finally, we evaluated our approach in an actual robotic environment showing that our architecture indeed reduces the overall computational time
A Uniform Axiomatic View of Lists, Multisets, and Sets, and the Relevant Unification Algorithms
No full text
2D object reconstruction with ASP
Full text linkDamages to cultural heritage due to human malicious actions or to natural disasters (e.g., earthquakes, tornadoes) are nowadays more and more frequent. Huge work is needed by professional restores to reproduce, as best as possible, the original artwork or architecture opera starting from the potsherds. The tool we are presenting in this paper is devised for being a digital support for this kind of work. As soon as the fragments of the opera are cataloged, a user (possibly young students, and even children, using a tablet or a smartphone as playing with a video game) can propose a partial reconstruction. The final part of the job is left to an ASP program that first computes a pre-processing task to find coherence between (sides of) fragments, and then tries to reconstruct the original object. Experiments are made here focusing on 2D reconstruction (frescoes, reliefs, etc)
- …
