Ottimizzazione Combinatorica mediante Deep Reinforcement Learning: Sperimentazione nella Logistica di Magazzino.

Il Deep Reinforcement Learning acquista sempre più importanza tra gli algoritmi di apprendimento dopo i brillanti risultati ottenuti da DeepMind con AlphaGo e AlphaZero, rispettivamente riuscendo a battere il campione mondiale di Go (2015) e il chess engine Stockfish (2017). Negli ultimi anni sono stati sviluppati maggiormente programmi in grado di giocare a numerosi giochi Atari e, nell'ambito della robotica, di ottenere ottimi risultati nell’autoapprendimento di specifici comportamenti. Sebbene questa nuova tecnologia sia stata applicata con successo in questi ambiti, sono scarse le ricerche inerenti l'utilizzo del Deep Reinforcement Learning su problemi di ottimizzazione combinatorica. Questa tesi si pone l’obiettivo di esplorare una possibile soluzione al problema reale dell’allocazione di prodotti in un magazzino, confrontando i risultati ottenuti con la Ricerca Operativa e con l’allocazione dell’azienda presa in considerazione per comprenderne la bontà. Nel corso di questo lavoro verrà dapprima introdotto il Reinforcement Learning in generale e in particolare il Q-learning. Successivamente verrà mostrato il Deep Reinforcement Learning, con un esempio applicandolo al gioco Catch. Infine sarà presentato il progetto e i risultati ottenuti del Deep Reinforcement Learning applicato avarie istanze del problema di allocazione di prodotti in magazzino

Bridging Symbolic and Sub-Symbolic AI: Towards Cooperative Transfer Learning in Multi-Agent Systems

Cooperation and knowledge sharing are of paramount importance in the evolution of an intelligent species. Knowledge sharing requires a set of symbols with a shared interpretation, enabling effective communication supporting cooperation. The engineering of intelligent systems may then benefit from the distribution of knowledge among multiple components capable of cooperation and symbolic knowledge sharing. Accordingly, in this paper, we propose a roadmap for the exploitation of knowledge representation and sharing to foster higher degrees of artificial intelligence. We do so by envisioning intelligent systems as composed by multiple agents, capable of cooperative (transfer) learning—Co(T)L for short. In CoL, agents can improve their local (sub-symbolic) knowledge by exchanging (symbolic) information among each others. In CoTL, agents can also learn new tasks autonomously by sharing information about similar tasks. Along this line, we motivate the introduction of Co(T)L and discuss benefits and feasibility

KINS: Knowledge Injection via Network Structuring

We propose a novel method to inject symbolic knowledge in form of Datalog formulæ into neural networks (NN), called KINS (Knowledge Injection via Network Structuring). The idea behind our method is to extend NN internal structure with ad-hoc layers built out the injected symbolic knowledge. KINS does not constrain NN to any specific architecture, neither requires logic formulæ to be ground. Moreover, it is robust w.r.t. both lack of data and imperfect/incomplete knowledge. Experiments are reported to demonstrate the potential of KINS

Symbolic Knowledge Injection meets Intelligent Agents: QoS metrics and experiments

Bridging intelligent symbolic agents and sub-symbolic predictors is a long-standing research goal in AI. Among the recent integration efforts, symbolic knowledge injection (SKI) proposes algorithms aimed at steering sub-symbolic predictors’ learning towards compliance w.r.t. pre-existing symbolic knowledge bases. However, state-of-the-art contributions about SKI mostly tackle injection from a foundational perspective, often focussing solely on improving the predictive performance of the sub-symbolic predictors undergoing injection. Technical contributions, in turn, are tailored on individual methods/experiments and therefore poorly interoperable with agent technologies as well as among each others. Intelligent agents may exploit SKI to serve many purposes other than predictive performance alone—provided that, of course, adequate technological support exists: for instance, SKI may allow agents to tune computational, energetic, or data requirements of sub-symbolic predictors. Given that different algorithms may exist to serve all those many purposes, some criteria for algorithm selection as well as a suitable technology should be available to let agents dynamically select and exploit the most suitable algorithm for the problem at hand. Along this line, in this work we design a set of quality-of-service (QoS) metrics for SKI, and a general-purpose software API to enable their application to various SKI algorithms—namely, platform for symbolic knowledge injection (PSyKI). We provide an abstract formulation of four QoS metrics for SKI, and describe the design of PSyKI according to a software engineering perspective. Then we discuss how our QoS metrics are supported by PSyKI. Finally, we demonstrate the effectiveness of both our QoS metrics and PSyKI via a number of experiments, where SKI is both applied and assessed via our proposed API. Our empirical analysis demonstrates both the soundness of our proposed metrics and the versatility of PSyKI as the first software tool supporting the application, interchange, and numerical assessment of SKI techniques. To the best of our knowledge, our proposals represent the first attempt to introduce QoS metrics for SKI, and the software tools enabling their practical exploitation for both human and computational agents. In particular, our contributions could be exploited to automate and/or compare the manifold SKI algorithms from the state of the art. Hence moving a concrete step forward the engineering of efficient, robust, and trustworthy software applications that integrate symbolic agents and sub-symbolic predictors

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges

An information theory analysis of critical Boolean networks as control software for robots

This work is an analysis of critical random Boolean networks used as control software for robots. The main goal is to find if there are relations between information theory measures on robot's sensors and actuators and the capability of the robot to achieve a particular task. Secondary goals are to verify if just the number of nodes of the networks is significant to obtain better populations of controllers for a given task and if a Boolean network can perform well in more than one single task. Results show that for certain tasks there is a strongly positively correlation between some information theory measures and the objective function of the task. Moreover Boolean networks with an higher number of nodes tend to perform better. These results can be useful in the automatic design process of control software for robots. Finally some Boolean networks from a random generated population exhibit phenotypic plasticity, which is the ability to manifest more phenotypes from the same genotype in different environments. In this scenario it is the capability of the same Boolean network (same functions and connections) to successfully achieve different tasks

Using WordNet Predicates for Multilingual Named Entity Recognition

WordNet predicates (WN-PREDS) establish relations between words in a certain language and concepts of a language independent ontology. In this paper we show how WN-PREDS can be profitably used in the context of multilingual tasks where two or more wordnets are aligned. Specifically, we report about the extension to Italian of a previously developed Named Entity Recognition (NER) system for written English. Experimental results demonstrate the validity of the approach and confirm the suitability of WN-PREDS for a number of different NLP tasks

Is it the right answer?: exploiting web redundancy for Answer Validation

Answer Validation is an emerging topic in Question Answering, where open domain systems are often required to rank huge amounts of candidate answers. We present a novel approach to answer validation based on the intuition that the amount of implicit knowledge which connects an answer to a question can be quantitatively estimated by exploiting the redundancy of Web information. Experiments carried out on the TREC-2001 judged-answer collection show that the approach achieves a high level of performance (i.e. 81\% success rate). The simplicity and the efficiency of this approach make it suitable to be used as a module in Question Answering systems

Open Domain Question/Answering on the Web

This paper presents a question/answering system for searching the web. The system accepts natural language questions in Italian and returns as answer a ranked list of document passages together with the URL of the whole document. Three crucial aspects related to the web scenario have been investigated: the linguistic expansion of the query, the optimization of the search boolean expressions, the evaluation of the results

On the Design of PSyKI: a Platform for Symbolic Knowledge Injection into Sub-Symbolic Predictors

A long-standing ambition in artificial intelligence is to integrate predictors’ inductive features (i.e., learning from examples) with deductive capabilities (i.e., drawing inferences from symbolic knowledge). Many methods in the literature support injection of symbolic knowledge into predictors, generally following the purpose of attaining better (i.e., more effective or efficient w.r.t. predictive performance) predictors. However, to the best of our knowledge, running implementations of these algorithms are currently either proof of concepts or unavailable in most cases. Moreover, a unified, coherent software framework supporting them as well as their interchange, comparison, and exploitation in arbitrary ML workflows is currently missing. Accordingly, in this paper we present the design of PSyKI, a platform providing general-purpose support to symbolic knowledge injection into predictors via different algorithms. In particular, we discuss the overall architecture, and the many components/functionalities of PSyKI, invidually—providing examples as well. We finally demonstrate the versatility of our approach by exemplifying two custom injection algorithms in a toy scenario: Poker Hands classification