Exploiting Evolutionary Modeling to Prevail in Iterated Prisoner’s Dilemma Tournaments

The iterated prisoner’s dilemma is a famous model of cooperation and conflict in game theory. Its origin can be traced back to the Cold War, and countless strategies for playing it have been proposed so far, either designed by hand or automatically generated by computers. In the 2000s, scholars started focusing on adaptive players, that is, able to classify their opponent’s behavior and adopt an effective counter-strategy. The player presented in this paper, pushes such idea even further: it builds a model of the current adversary from scratch, without relying on any pre-defined archetypes, and tweaks it as the game develops using an evolutionary algorithm; at the same time, it exploits the model to lead the game into the most favorable continuation. Models are compact non-deterministic finite state machines; they are extremely efficient in predicting opponents’ replies, without being completely correct by necessity. Experimental results show that such player is able to win several one-to- one games against strong opponents taken from the literature, and that it consistently prevails in round-robin tournaments of different sizes

Why and How Using HPC in University Teaching? A Case Study at PoliTo

After an era of “personal-computers-only”, supercomputing facilities and services are coming back to Universities to support research activities and computationally intensive simulations, but with some important differences with respect to the past. Besides the technological issues, while in the seventies-eighties the scene was dominated by mainframes, managed by skilled system managers and most of times used by operators with good computer expertise, today, the widespread and pervasive use of computers has lead to a completely different scenario. The demand for computation resources is emerging from a wide variety of areas and disciplines and mostly by users with basic expertise in computers, who however most of times request to have full control of the computation resources. Within this picture and especially at University level, High Performance Computing (HPC) has emerged as a good tradeoff to meet the different demands, and at the same offering good services at reasonable setup and maintenance costs. Traditionally, HPC has been and is being mostly used in support to applied research, but more recently some questions have emerged: - How much is it reasonable to offer HPC also to some teaching activities? What are the problems, advantages, and drawbacks? Is this the “right” way or should HPC resources be directed to research only? At Politecnico di Torino we tried to respond to these questions and started a test project called HPC-4-teaching. In this paper we present the results achieved by this project on a small set of courses during one full academic year

Thinking BigData: Motivation, Results and a Few Recipes for a Balanced Growth of High Performance Computing in Academia

Big Data is today both an emerging research area and a real present and future demand. High Performance Computing (HPC) Centers cannot neglect this fact and have to be reshaped to fulfill this need. In this paper we share our experience of upgrading a HPC Center at Politecnico di Torino, originally designed and deployed in 2010. We believe that this issue could be common to some other existing "general purpose" HPC centers where, at least in the short term, the possibility to start from scratch a new Big Data HPC center cannot be afforded but a balanced upgrade of the existing system has to be preferre

Colorectal Cancer Stage at Diagnosis Before vs During the COVID-19 Pandemic in Italy

IMPORTANCE Delays in screening programs and the reluctance of patients to seek medical attention because of the outbreak of SARS-CoV-2 could be associated with the risk of more advanced colorectal cancers at diagnosis. OBJECTIVE To evaluate whether the SARS-CoV-2 pandemic was associated with more advanced oncologic stage and change in clinical presentation for patients with colorectal cancer. DESIGN, SETTING, AND PARTICIPANTS This retrospective, multicenter cohort study included all 17 938 adult patients who underwent surgery for colorectal cancer from March 1, 2020, to December 31, 2021 (pandemic period), and from January 1, 2018, to February 29, 2020 (prepandemic period), in 81 participating centers in Italy, including tertiary centers and community hospitals. Follow-up was 30 days from surgery. EXPOSURES Any type of surgical procedure for colorectal cancer, including explorative surgery, palliative procedures, and atypical or segmental resections. MAIN OUTCOMES AND MEASURES The primary outcome was advanced stage of colorectal cancer at diagnosis. Secondary outcomes were distant metastasis, T4 stage, aggressive biology (defined as cancer with at least 1 of the following characteristics: signet ring cells, mucinous tumor, budding, lymphovascular invasion, perineural invasion, and lymphangitis), stenotic lesion, emergency surgery, and palliative surgery. The independent association between the pandemic period and the outcomes was assessed using multivariate random-effects logistic regression, with hospital as the cluster variable. RESULTS A total of 17 938 patients (10 007 men [55.8%]; mean [SD] age, 70.6 [12.2] years) underwent surgery for colorectal cancer: 7796 (43.5%) during the pandemic period and 10 142 (56.5%) during the prepandemic period. Logistic regression indicated that the pandemic period was significantly associated with an increased rate of advanced-stage colorectal cancer (odds ratio [OR], 1.07; 95%CI, 1.01-1.13; P = .03), aggressive biology (OR, 1.32; 95%CI, 1.15-1.53; P < .001), and stenotic lesions (OR, 1.15; 95%CI, 1.01-1.31; P = .03). CONCLUSIONS AND RELEVANCE This cohort study suggests a significant association between the SARS-CoV-2 pandemic and the risk of a more advanced oncologic stage at diagnosis among patients undergoing surgery for colorectal cancer and might indicate a potential reduction of survival for these patients

Introduzione all'Informatica e Linguaggio C

Testo destinato all'insegnamento dell'informatica di base, intende fornire le cognizioni fondamentali per operare con efficacia e consapevolezza con gli strumenti informatici, curando particolarmente gli aspetti sperimentali della disciplina. E' mirato all'acquisizione delle nozioni di base sui meccanismi di funzionamento dei sistemi di elaborazione e dei paradigmi fondamentali della programmazione

Adaptive opponent modelling for the iterated prisoner's dilemma

This paper describes the design of Laran, an intelligent player for the iterated prisoner's dilemma. Laran is based on an evolutionary algorithm, but instead of using evolution as a mean to define a suitable strategy, it uses evolution to model the behavior of its adversary. In some sense, it understands its opponent, and then exploits such knowledge to devise the best possible conduct. The internal model of the opponent is continuously adapted during the game to match the actual outcome of the game, taking into consideration all played actions. Whether the model is correct, Laran is likely to gain constant advantages and eventually win. A prototype of the proposed approach was matched against twenty players implementing state-of-the art strategies. Results clearly demonstrated the claims