Palo Alto Research Center, Mail Stop: 3333 Coyote Hill Road, Palo Alto, CA 94034 USA Abstract Our research aims at developing intelligent systems to reduce the transportation-related energy expenditure of a large city by influencing individual behavior. We introduce Copter - an intelligent travel assistant that evaluates multi-modal travel alternatives to find a plan that is acceptable to a person given their context and preferences. We propose a formulation for acceptable planning that brings together ideas from AI, machine learning, and economics. This formulation has been incorporated in Copter that produces acceptable plans in real-time. We adopt a novel empirical evaluation framework that combines human decision data with a high fidelity multi-modal transportation simulation to demonstrate a 4% energy reduction and 20% delay reduction in a realistic deployment scenario in Los Angeles, California, USA. 1. Introduction Transportation is one of the largest consumers of energy in the ...

In this paper, we introduce and analyze new envy-based fairness concepts for agents with weights: these weights regulate their mutual envy in a situation where indivisible goods are allocated to the agents. We propose two variants of envy-freeness up to one item for the weighted setting: in the strong variant, the envy can be eliminated by removing an item from the envied agent's bundle, whereas in the weak variant, envy can be eliminated by either removing an item from the envied agent's bundle or by replicating an item from the envied agent's bundle in the envying agent's bundle. We prove that for additive valuations, a strongly weighted envy-free allocation up to one item always exists and can be efficiently computed by means of a weight-based picking sequence. For two agents, we can also efficiently achieve strong weighted envy-freeness up to one item in conjunction with Pareto optimality using a weighted version of the classic adjusted winner algorithm. In addition, we show that an allocation that maximizes the weighted Nash social welfare always satisfies weak weighted envy-freeness up to one item, but may fail to satisfy the strong version of this property.

The ability to interpret the mental state of another agent based on its behavior, also called Theory of Mind (ToM), is crucial for humans in any kind of social interaction. Artificial systems, such as intelligent assistants, would also greatly benefit from such mentalizing capabilities. However, humans and systems alike are bound by limitations in their available computational resources. This raises the need for satisficing mentalizing, reconciling accuracy and efficiency in mental state inference that is good enough for a given situation. In this paper, we present different Bayesian models of ToM reasoning and evaluate them based on actual human behavior data that were generated under different kinds of uncertainties. We propose a Switching approach that combines specialized models, embodying simplifying presumptions, in order to achieve a more statisficing mentalizing compared to a Full Bayesian ToM model.

This work focuses on studying players behaviour in interactive narratives with the aim to simulate their choices. Besides sub-optimal player behaviour due to limited knowledge about the environment, the difference in each player's style and preferences represents a challenge when trying to make an intelligent system mimic their actions. Based on observations from players interactions with an extract from the interactive fiction Anchorhead, we created a player profile to guide the behaviour of a generic player model based on the BDI (Belief-Desire-Intention) model of agency. We evaluated our approach using qualitative and quantitative methods and found that the player profile can improve the performance of the BDI player model. However, we found that players self-assessment did not yield accurate data to populate their player profile under our current approach.

In this paper, we present a new modality for intelligent inte r-active narratives within the theatre domain. We discuss the possibilities of using an intelligent agent that serves as a drama manager a nd as an actor that plays a character within the live theatre exper ience. We pose a set of research challenges that arise from our analysi s towards the implementation of such an agent, as well as potential method ologies as a starting point to bridge the gaps between current literatu re and the proposed modality.

Noname manuscript No. (will be inserted by the editor)Active Collaboration in Relative Observation for Multi-agent Visual SLAM based on Deep Q Network Zhaoyi Pei · Piaosong Hao · Meixiang Quan · Muhammad Zuhair Qadir · Guo Li Received: date / Accepted: date Abstract This paper proposes a unique active relative localization mechanism for multi-agent Simultaneous Localization and Mapping(SLAM),in which a agent to be observed are considered as a task, which is performed by others assisting that agent by relative observation. A task allocation algorithm based on deep reinforcement learning are proposed for this mechanism. Each agent can choose whether to localize other agents or to continue independent SLAM on it own initiative. By this way, the process of each agent SLAM will be interacted by the collaboration. Firstly, based on the characteristics of ORBSLAM, a unique observation function which models the whole MAS is obtained. Secondly, a novel type of Deep Q network(DQN) called MAS-DQN is deployed to learn correspondence between Q Value and state-action pair, abstract representation of agents in MAS are learned in the process of collaboration amongZhaoyi Pei Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China Email: peizhaoyi@stu.hit.edu.cn Songhao Piao Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China Email: piaosh@hit.edu.cn Meixiang Quan Department of Computer Science and Technology, Harbin Institute of Technology, Harbin, China Email: 15b903042@hit.edu.cn

A novel model developed by MIT and Microsoft researchers identifies instances in which autonomous systems have "learned" from training examples that don't match what's actually happening in the real world. Engineers could use this model to improve the safety of artificial intelligence systems, such as driverless vehicles and autonomous robots. The AI systems powering driverless cars, for example, are trained extensively in virtual simulations to prepare the vehicle for nearly every event on the road. But sometimes the car makes an unexpected error in the real world because an event occurs that should, but doesn't, alter the car's behavior. Consider a driverless car that wasn't trained, and more importantly doesn't have the sensors necessary, to differentiate between distinctly different scenarios, such as large, white cars and ambulances with red, flashing lights on the road.

Following sexual assault allegations Antonio Brown has been released from the Patriots. Antonio Brown has been cut loose by two NFL teams this month amid a list of controversies that include sexual assault and rape allegations – but some of the other teams in the league are still expressing interest in his services as a top-flight wide receiver, according to his agent. However, the unnamed teams said to be interested in Brown "want information regarding his legal situation and the NFL investigation" into the accusations made against him, Drew Rosenhaus told ESPN on Saturday. Brown, 31, a seven-time Pro Bowl player, was let go by the New England Patriots on Friday, after a lawyer representing one of his female accusers alerted the NFL about allegedly "intimidating" emails believed to have been sent to the woman by Brown earlier in the week. Not long after Brown was let go, the NFL issued a statement regarding the status of Brown's relationship with the league.

This paper presents an initial design concept and specification of a civilian Unmanned Aerial Vehicle (UAV) management simulation system that focuses on explainability for the human-in-the-loop control of semi-autonomous UAVs. The goal of the system is to facilitate the operator intervention in critical scenarios (e.g. avoid safety issues or financial risks). Explainability is supported via user-friendly abstractions on Belief-Desire-Intention agents. To evaluate the effectiveness of the system, a human-computer interaction study is proposed.

Game Theory, quite unlike its name, is a serious affair to deal with when it comes to the configuration and planning of an AI model. In essence, while linear machine learning deals largely with single-dimensional elements in their very nature, the true power of AI is actually unleashed with game theory application, and it's various facets. To understand game theory power in AI, however, it is essential to understand the basics of what actually constitutes game theory and its applications. So here's the promised primer on what game theory actually comprises. In its textbook definition, "Game Theory is the study of strategic interaction".