Meta-Referential Games to Learn Compositional Learning Behaviours

Human beings use compositionality to generalise from past experiences to novel experiences. We assume a separation of our experiences into fundamental atomic components that can be recombined in novel ways to support our ability to engage with novel experiences. We frame this as the ability to learn to generalise compositionally, and we will refer to behaviours making use of this ability as compositional learning behaviours (CLBs). A central problem to learning CLBs is the resolution of a binding problem (BP). While it is another feat of intelligence that human beings perform with ease, it is not the case for state-of-the-art artificial agents. Thus, in order to build artificial agents able to collaborate with human beings, we propose to develop a novel benchmark to investigate agents' abilities to exhibit CLBs by solving a domain-agnostic version of the BP. We take inspiration from the language emergence and grounding framework of referential games and propose a meta-learning extension of referential games, entitled Meta-Referential Games, and use this framework to build our benchmark, that we name Symbolic Behaviour Benchmark (S2B). We provide baseline results showing that our benchmark is a compelling challenge that we hope will spur the research community towards developing more capable artificial agents.Comment: work in progres

ETHER: Aligning Emergent Communication for Hindsight Experience Replay

Natural language instruction following is paramount to enable collaboration between artificial agents and human beings. Natural language-conditioned reinforcement learning (RL) agents have shown how natural languages' properties, such as compositionality, can provide a strong inductive bias to learn complex policies. Previous architectures like HIGhER combine the benefit of language-conditioning with Hindsight Experience Replay (HER) to deal with sparse rewards environments. Yet, like HER, HIGhER relies on an oracle predicate function to provide a feedback signal highlighting which linguistic description is valid for which state. This reliance on an oracle limits its application. Additionally, HIGhER only leverages the linguistic information contained in successful RL trajectories, thus hurting its final performance and data-efficiency. Without early successful trajectories, HIGhER is no better than DQN upon which it is built. In this paper, we propose the Emergent Textual Hindsight Experience Replay (ETHER) agent, which builds on HIGhER and addresses both of its limitations by means of (i) a discriminative visual referential game, commonly studied in the subfield of Emergent Communication (EC), used here as an unsupervised auxiliary task and (ii) a semantic grounding scheme to align the emergent language with the natural language of the instruction-following benchmark. We show that the referential game's agents make an artificial language emerge that is aligned with the natural-like language used to describe goals in the BabyAI benchmark and that it is expressive enough so as to also describe unsuccessful RL trajectories and thus provide feedback to the RL agent to leverage the linguistic, structured information contained in all trajectories. Our work shows that EC is a viable unsupervised auxiliary task for RL and provides missing pieces to make HER more widely applicable.Comment: work in progres

Semantic Data Augmentation for Deep Learning Testing using Generative AI

he performance of state-of-the-art Deep Learning models heavily depends on the availability of well-curated training and testing datasets that sufficiently capture the operational domain. Data augmentation is an effective technique in alleviating data scarcity, reducing the time-consuming and expensive data collection and labelling processes. Despite their potential, existing data augmentation techniques primarily focus on simple geometric and colour space transformations, like noise, flipping and resizing, producing datasets with limited diversity. When the augmented dataset is used for testing the Deep Learning models, the derived results are typically uninformative about the robustness of the models. We address this gap by introducing GENFUZZER, a novel coverage-guided data augmentation fuzzing technique for Deep Learning models underpinned by generative AI. We demonstrate our approach using widely-adopted datasets and models employed for image classification, illustrating its effectiveness in generating informative datasets leading up to a 26% increase in widely-used coverage criteria

Interactive storytelling for children: A case-study of design and development considerations for ethical conversational AI

Conversational Artificial Intelligence (CAI) systems and Intelligent Personal Assistants (IPA), such as Alexa, Cortana, Google Home and Siri are becoming ubiquitous in our lives, including those of children, the implications of which is receiving increased attention, specifically with respect to the effects of these systems on children’s cognitive, social and linguistic development. Recent ad- vances address the implications of CAI with respect to privacy, safety, security, and access. However, there is a need to connect and embed the ethical and technical aspects in the design. Using a case-study of a research and develop- ment project focused on the use of CAI in storytelling for children, this paper reflects on the social context within a specific case of technology development, as substantiated and supported by argumentation from within the literature. It describes the decision making process behind the recommendations made on this case for their adoption in the creative industries. Further research that engages with developers and stakeholders in the ethics of storytelling through CAI is highlighted as a matter of urgency

On-line aggregation of POIs from google and facebook

In the last decade, social media have become widely exploited sources of information concerning every aspect of life. The reason is that they permit to create and widely share information, even in mobility by exploiting mobile apps. Information available on social media are often related to public places (restaurants, museums, etc.), and users often look for interesting public places. Currently, various social media own and publish huge and independently-built corpora of geo-located data about public places, which are not linked each other. In particular, the main players are Google and Facebook. Users searching for a public place of interest (POI) might wish to get all available information from social media. Therefore, they need an on-line aggregation engine for public places that returns an aggregated view of a place, retrieving data concerning the same place from various sources. The on-line approach is suggested by continuous variations in data within the on-line corpora, that demands for a technique that cannot rely on off-line processing. In this paper, we address the problem by devising a novel technique to aggregate geo-located data about public places; the application context is to associate data provided by Google Places with Facebook pages concerning public places; the Klondike software tool implements this technique. Tests were conducted on a data set containing about 300 public places in Manchester (UK)

Time to Die 2: Improved in-game death prediction in Dota 2

Competitive video game playing, an activity called esports, is increasingly popular to the point that there are now many professional competitions held for a variety of games. These competitions are broadcast in a professional manner similar to traditional sports broadcasting. Esports games are generally fast paced, and due to the virtual nature of these games, camera positioning can be limited. Therefore, knowing ahead of time where to position cameras, and what to focus a broadcast and associated commentary on, is a key challenge in esports reporting. This gives rise to moment-to-moment prediction within esports matches which can empower broadcasters to better observe and process esports matches. In this work we focus on this moment-to-moment prediction and in particular present techniques for predicting if a player will die within a set number of seconds for the esports title Dota 2. A player death is one of the most consequential events in Dota 2. We train our model on ‘telemetry’ data gathered directly from the game itself, and position this work as a novel extension of our previous work on the challenge. We use an enhanced dataset covering 9,822 Dota 2 matches. Since the publication of our previous work, new dataset parsing techniques developed by the WEAVR project enable the model to track more features, namely player status effects, and more importantly, to operate in real time. Additionally, we explore two new enhancements to the original model: one data-based extension and one architectural. Firstly we employ learnt embeddings for categorical features, e.g. which in game character a player has selected, and secondly we explicitly model the temporal element of our telemetry data using recurrent neural networks. We find that these extensions and additional features all aid the predictive power of the model achieving an F1 score of 0.54 compared to 0.17 for our previous model (on the new data). We improve this further by experimenting with the length of the time-series in the input data and find using 15 time steps further improves the F1 score to 0.62. This compares to F1 of 0.1 for a standard RNN on the same task. Additionally a deeper analysis of the Time to Die model is carried out to assess its suitability as a broadcast aid