Accurately predicting the possible behaviors of traffic participants is an essential capability for autonomous vehicles. Since autonomous vehicles need to navigate in dynamically changing environments, they are expected to make accurate predictions regardless of where they are and what driving circumstances they encountered. A number of methodologies have been proposed to solve prediction problems under different traffic situations. However, these works either focus on one particular driving scenario (e.g. highway, intersection, or roundabout) or do not take sufficient environment information (e.g. road topology, traffic rules, and surrounding agents) into account. In fact, the limitation to certain scenario is mainly due to the lackness of generic representations of the environment. The insufficiency of environment information further limits the flexibility and transferability of the predictor. In this paper, we propose a scenario-transferable and interaction-aware probabilistic prediction algorithm based on semantic graph reasoning, which predicts behaviors of selected agents. We put forward generic representations for various environment information and utilize them as building blocks to construct their spatio-temporal structural relations. We then take the advantage of these structured representations to develop a flexible and transferable prediction algorithm, where the predictor can be directly used under unforeseen driving circumstances that are completely different from training scenarios. The proposed algorithm is thoroughly examined under several complicated real-world driving scenarios to demonstrate its flexibility and transferability with the generic representation for autonomous driving systems.

Multi-Agent Reinforcement Learning (MARL) methods find optimal policies for agents that operate in the presence of other learning agents. Central to achieving this is how the agents coordinate. One way to coordinate is by learning to communicate with each other. Can the agents develop a language while learning to perform a common task? In this paper, we formulate and study a MARL problem where cooperative agents are connected to each other via a fixed underlying network. These agents can communicate along the edges of this network by exchanging discrete symbols. However, the semantics of these symbols are not predefined and, during training, the agents are required to develop a language that helps them in accomplishing their goals. We propose a method for training these agents using emergent communication. We demonstrate the applicability of the proposed framework by applying it to the problem of managing traffic controllers, where we achieve state-of-the-art performance as compared to a number of strong baselines. More importantly, we perform a detailed analysis of the emergent communication to show, for instance, that the developed language is grounded and demonstrate its relationship with the underlying network topology. To the best of our knowledge, this is the only work that performs an in depth analysis of emergent communication in a networked MARL setting while being applicable to a broad class of problems.

The connection between messaging and action is fundamental both to web applications, such as web search and sentiment analysis, and to economics. However, while prominent online applications exploit messaging in natural (human) language in order to predict non-strategic action selection, the economics literature focuses on the connection between structured stylized messaging to strategic decisions in games and multi-agent encounters. This paper aims to connect these two strands of research, which we consider highly timely and important due to the vast online textual communication on the web. Particularly, we introduce the following question: can free text expressed in natural language serve for the prediction of action selection in an economic context, modeled as a game? In order to initiate the research on this question, we introduce the study of an individual's action prediction in a one-shot game based on free text he/she provides, while being unaware of the game to be played. We approach the problem by attributing commonsensical personality attributes via crowd-sourcing to free texts written by individuals, and employing transductive learning to predict actions taken by these individuals in one-shot games based on these attributes. Our approach allows us to train a single classifier that can make predictions with respect to actions taken in multiple games. In experiments with three well-studied games, our algorithm compares favorably with strong alternative approaches. In ablation analysis, we demonstrate the importance of our modeling choices -- the representation of the text with the commonsensical personality attributes and our classifier -- to the predictive power of our model.

Potential Based Reward Shaping combined with a potential function based on appropriately defined abstract knowledge has been shown to significantly improve learning speed in Reinforcement Learning. MultiGrid Reinforcement Learning (MRL) has further shown that such abstract knowledge in the form of a potential function can be learned almost solely from agent interaction with the environment. However, we show that MRL faces the problem of not extending well to work with Deep Learning. In this paper we extend and improve MRL to take advantage of modern Deep Learning algorithms such as Deep Q-Networks (DQN). We show that DQN augmented with our approach perform significantly better on continuous control tasks than its Vanilla counterpart and DQN augmented with MRL.

Norm emergence is typically studied in the context of multiagent systems (MAS) where norms are implicit, and participating agents use simplistic decision-making mechanisms. These implicit norms are usually unconsciously shared and adopted through agent interaction. A norm is deemed to have emerged when a threshold or predetermined percentage of agents follow the "norm". Conversely, in normative MAS, norms are typically explicit and agents deliberately share norms through communication or are informed about norms by an authority, following which an agent decides whether to adopt the norm or not. The decision to adopt a norm by the agent can happen immediately after recognition or when an applicable situation arises. In this paper, we make the case that, similarly, a norm has emerged in a normative MAS when a percentage of agents adopt the norm. Furthermore, we posit that agents themselves can and should be involved in norm synthesis, and hence influence the norms governing the MAS, in line with Ostrom's eight principles. Consequently, we put forward a framework for the emergence of norms within a normative MAS, that allows participating agents to propose/request changes to the normative system, while special-purpose synthesizer agents formulate new norms or revisions in response to these requests. Synthesizers must collectively agree that the new norm or norm revision should proceed, and then finally be approved by an "Oracle". The normative system is then modified to incorporate the norm.

We introduce a framework for reaching a consensus amongst several agents communicating via a trust network on conflicting information about their environment. We formalise our approach and provide an empirical and theoretical analysis of its properties.

UNESCO has appointed Moroccan artificial intelligence expert, Mrs. Amal El Fallah Seghrouchni, to the World Commission on the Ethics of Scientific Knowledge and Technology (COMEST). The Moroccan researcher joins the commission for a four-year term, from 2020 to 2023. "It is an honor for me to serve ethics within this beautiful institution that is UNESCO," Seghrouchni shared on Twitter. The researcher holds a doctorate in artificial intelligence from the Pierre and Marie Curie University in Paris. She is professor at the School of Science and Engineering of Sorbonne University.

We propose a new representation setting for hedonic games, where each agent partitions the set of other agents into friends, enemies, and neutral agents, with friends and enemies being ranked. Under the assumption that preferences are monotonic (respectively, antimonotonic) with respect to the addition of friends (respectively, enemies), we propose a bipolar extension of the responsive extension principle, and use this principle to derive the (partial) preferences of agents over coalitions. Then, for a number of solution concepts, we characterize partitions that necessarily or possibly satisfy them, and we study the related problems in terms of their complexity.

The trend is to implement intelligent agents capable of analyzing available information and utilize it efficiently. This work presents a number of reinforcement learning (RL) architectures; one of them is designed for intelligent agents. The proposed architectures are called selector-actor-critic (SAC), tuner-actor-critic (TAC), and estimator-selector-actor-critic (ESAC). These architectures are improved models of a well known architecture in RL called actor-critic (AC). In AC, an actor optimizes the used policy, while a critic estimates a value function and evaluate the optimized policy by the actor. SAC is an architecture equipped with an actor, a critic, and a selector. The selector determines the most promising action at the current state based on the last estimate from the critic. TAC consists of a tuner, a model-learner, an actor, and a critic. After receiving the approximated value of the current state-action pair from the critic and the learned model from the model-learner, the tuner uses the Bellman equation to tune the value of the current state-action pair. ESAC is proposed to implement intelligent agents based on two ideas, which are lookahead and intuition. Lookahead appears in estimating the values of the available actions at the next state, while the intuition appears in maximizing the probability of selecting the most promising action. The newly added elements are an underlying model learner, an estimator, and a selector. The model learner is used to approximate the underlying model. The estimator uses the approximated value function, the learned underlying model, and the Bellman equation to estimate the values of all actions at the next state. The selector is used to determine the most promising action at the next state, which will be used by the actor to optimize the used policy. Finally, the results show the superiority of ESAC compared with the other architectures.

Supervised learning methods excel at capturing statistical properties of language when trained over large text corpora. Yet, these models often produce inconsistent outputs in goal-oriented language settings as they are not trained to complete the underlying task. Moreover, as soon as the agents are finetuned to maximize task completion, they suffer from the so-called language drift phenomenon: they slowly lose syntactic and semantic properties of language as they only focus on solving the task. In this paper, we propose a generic approach to counter language drift by using iterated learning. We iterate between fine-tuning agents with interactive training steps, and periodically replacing them with new agents that are seeded from last iteration and trained to imitate the latest finetuned models. Iterated learning does not require external syntactic constraint nor semantic knowledge, making it a valuable task-agnostic finetuning protocol. We first explore iterated learning in the Lewis Game. We then scale-up the approach in the translation game. In both settings, our results show that iterated learn-ing drastically counters language drift as well as it improves the task completion metric.