Spain

&quot;When robots undertake tasks in adversarial environments in which they must cooperate with one another (e.g., military applications or the RoboCup Competition), they are at risk for being deceived by competitors. Competi-tors can misdirect the team to gain a positional advantage. Our lab is explor-ing ways in which teams of robots can be misdirected, in part, so counter-deception strategies can be devised. This paper explores how robot shills can be used to misdirect a multi-robot team. It defines behaviors for the agents to be deceived (the mark agents) using the multi-agent coordination literature as well as behaviors for the deceiving team (the shills and lead agent). These behaviors were implemented and simulations were run for a variety of conditions. The results show how shills can facilitate misdirec-tion in certain circumstances. They provide insights into enhancing multi-robot team deception.
&quot;

ECAI 2020

Wolves in Sheep’s Clothing: Using Shill Agents to Misdirect Multi-Robot Teams

### Welcome to the 8th annual workshop on Naval Applications of Machine Learning!

NAML is the premier annual event showcasing current machine learning research relevant to Naval applications. We welcome participation from the defense community, industry, and academia. The NAML workshop is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team.

The goals of NAML are:

* To spread awareness of current machine learning research relevant to Naval applications

* To connect machine learning researchers with experts in military requirements

* To build and strengthen collaborations within the defense research community

Naval Information Warfare Center Pacific is partnering with AFCEA San Diego chapter to host the workshop.

**General Chairs:**

Katie Rainey, Naval Information Warfare Center Pacific

Nicole Isoda, Naval Information Warfare Center Pacific<br><br>

**[More about NAML 2024](https://sites.google.com/go.spawar.navy.mil/naml/)**
<br><br>

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NAML 2024

The 8th annual workshop on Naval Applications of Machine Learning is an opportunity for researchers from Navy research and development centers and our partner organizations to exchange technical ideas and to come together as a team. The goals of NAML are to spread awareness of current machine learning research relevant to Naval applications, to connect machine learning researchers with experts in military requirements and to build and strengthen collaborations within the defense research community.


"When robots undertake tasks in adversarial environments in which they must cooperate with one another (e.g., military applications or the RoboCup Competition), they are at risk for being deceived by competitors. Competi-tors can misdirect the team to gain a positional advantage. Our lab is explor-ing ways in which teams of robots can be misdirected, in part, so counter-deception strategies can be devised. This paper explores how robot shills can be used to misdirect a multi-robot team. It defines behaviors for the agents to be deceived (the mark agents) using the multi-agent coordination literature as well as behaviors for the deceiving team (the shills and lead agent). These behaviors were implemented and simulations were run for a variety of conditions. The results show how shills can facilitate misdirec-tion in certain circumstances. They provide insights into enhancing multi-robot team deception.
"

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

"In 2000, it was predicted that artificially intelligent agents would inevitably
become deceptive. Today, in a world seemingly awash with fake news
and in which we hand over control of our home environments to faux-human
smart devices, it is timely to review the types of deception that have actually
emerged. By reference to examples from diverse branches of AI, we classify research on deception into five novel categories, which we describe according to the human characteristic with which they most closely align: imitating, obfuscating,
tricking, calculating and reframing. We offer this as a way for those within AI
to recognise connections across the discipline and to suggest how AI-instigated
deceptions may be understood by those outside."

Characterising Deception in AI: a survey

Drawing on an ecological perspective, we contend that research into deception in AI needs to consider not only the cognitive structures of would-be deceptive agents but also the nature of the environments in which they act. To illustrate this approach, we report work-in-progress to design a game called MindTrails, played between a software agent and a human opponent, that is informed by the principles of stage magic to embed deceptive possibilities into its game world. MindTrails is intended to have well-defined elements and rules, while being complex enough to afford a rich range of deceptive behaviours. In this way, it allows us to more precisely articulate some of the deceptive principles of the stage magician and render them more accessible to AI methods and researchers.

The role of environments in affording deceptive behaviour: some preliminary insights from stage magic

A description about the topic, E-friend overview, theory of emotions, description of DAM module and an example of interaction between E-friend and student

E-friend: A logical-based AI Agent System Chat-bot for EmotionalWell-being and Mental Health (Paper #5)

"The goal of the work presented in this paper is to provide techniques to challenge the results of an algorithmic decision system relying on machine learning. We highlight the differences between explanations and justifications and outline a framework to generate evidence to support or to dismiss challenges. We also present the results of a preliminary study to assess users’ perception of the different types of challenges proposed here and their benefits to detect incorrect results.
"

Towards a framework for challenging ML-based decisions

Previous work in the social sciences, psychology and linguistics has show that liars have some control over the content of their stories, however their underlying state of mind may "leak out" through the way that they tell them. To the best of our knowledge, no previous systematic effort exists in order to describe and model deception language for Brazilian Portuguese. To fill this important gap, we carry out a pioneering empirical linguistic study on false statements in Brazilian news. We methodically analyze linguistic features using the Fake.Br corpus, which includes both fake and true news. The results show that they present substantial lexical, syntactic and semantic variations, as well as punctuation and emotion distinctions.

Studying Dishonest Intentions in Brazilian Portuguese Texts

"This research presents an analysis of AI adoption based on the analysis of the ProgrammableWeb directory. ProgrammableWeb.com is known as the Web’s most popular open Application programming interfaces (APIs) repository. Ever since it was founded in 2005, it has been chronicling the daily evolution of the global API, and it is the most relied-on directory when it comes to discovering and searching for APIs. With owner’s permission, we downloaded the ProgrammableWeb directory dataset, containing 21609 APIs, 1641 libraries, 15329 SDKs, 7629 mashups, and 10989 sample source codes. These resources were analysed to have an indication/measure on the AI adoption related to the API domain. Overall, the analysis showed increasing artificial intelligence adoption
trends from 2005 onwards."

Tracking of Artificial Intelligence Adoption in ProgrammableWeb Directory

"In this paper we introduce a methodology for identifying early warning signs of transformative progress in AI, to aid anticipatory governance and research prioritisation. We propose using expert elicitation methods to identify milestones in
AI progress, followed by collaborative causal mapping to identify key milestones which underpin several others. We call these key milestones ‘canaries’ based on the colloquial phrase ‘canary in a coal mine’ to describe advance warning of an extreme event: in this case, advance warning of transformative AI. After describing and motivating our proposed methodology, we present results from an initial implementation to identify canaries for progress towards high-level machine intelligence (HLMI). We conclude by discussing the limitations of this method, possible future improvements, and how we hope it can be used to improve monitoring of future risks from AI progress. "

Canaries in Technology Mines: Warning Signs of Transformative Progress in AI

"Competitions are a proven and cost-effective method to quickly develop new disruptive technologies for new markets. However, artificial intelligence and robotics competitions are usually organized independently, in silos. To get the most out of competitions, evaluation of robots should be modular (tasks are designed to assess independently different technological building blocks of the robotic systems) and open (evaluation tools and data must be publicly and freely available online). This not only meets the benchmarking needs of end users (mostly interested in performance measurements of the complete system), but also those of integrators and developers of intelligent components (camera, lidar, radar, actuators, etc.).
With this objective in mind, the H2020 METRICS project (2020-2023) organizes competitions in four application areas (Healthcare, Infrastructure Inspection and Maintenance, Agri-Food, and Agile Production) relying on both physical testing facilities (field evaluation campaign) and virtual testing facilities (data-based evaluation campaign) to mobilize, in addition to the European robotics community, the artificial intelligence one.
This presentation outlines this approach and paves the way for a new robotics and artificial intelligence competition paradigm."

AI evaluation campaigns during robotics competitions: the METRICS paradigm

"The ROSE Challenge is the first robotics and artificial intelligence competition worldwide to implement a third-party performance evaluation of intra-row weeding robots in real conditions, under comparable conditions, to guarantee a credible and objective assessment of their effectiveness. This article reports on the design and
validation of testing facilities for this competition, which presents a particular complexity : the experiments take place outdoors and act on living things (crops and weeds). Moreover, it implies to guarantee repeatable experimental conditions for a comparable and equitable evaluation. The article also discusses the opportunity that these competitions represent to define testing facilities in a consensual way.
The method it proposes is very widely applicable to different fields of intelligent systems applications."

Design and validation of testing facilities for weeding robots as part of ROSE challenge

"Recent progress in machine learning, on predictive tasks in particular, is usually claimed on the basis of performance comparisons.
Due to the limitation of application scenarios and computational resources, most people cannot evaluate their work on a large variety of tasks and datasets, and it has become tricky for both reviewers and readers to verify individual performance gains for a given approach.
In this paper we investigate possible approaches to achieve better efficiency on model benchmarking. 
For a large collection of datasets, rather than training and testing a given approach on every individual dataset, we seek methods that allow us to pick only a few representative datasets to quantify the model's goodness, from which to extrapolate to performance on other datasets. 
To this end, we adopt existing approaches from psychometrics: specifically, Item Response Theory and Adaptive Testing. Both are well-founded frameworks designed for educational tests.
We propose certain modifications following the requirements of machine learning experiments, and present some initial results and insights in this paper."

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