Alternative energy is becoming a growing source of power in the United States, including wind, hydroelectric and solar. The Solar Decathlon is a competition run by the US Department of Energy every two years. Washington State University (WSU) is one of twenty teams recently selected to compete in the fall 2017 challenge. A central part to WSU's entry is incorporating new and existing smart home technology from the ground up. The smart home can help to optimize energy loads, battery life and general comfort of the user in the home. This paper discusses the high-level goals of the project, hardware selected, build strategy and anticipated approach.

Recently a large attention has been devoted to the ethical issues arising around the design and the implementation of artificial agents. This is due to the fact that humans and machines more and more often need to collaborate to decide on actions to take or decisions to make. Such decisions should be not only correct and optimal from the point of view of the overall goal to be reached, but should also agree to some form of moral values which are aligned to the human ones. Examples of such scenarios can be seen in autonomous vehicles, medical diagnosis support systems, and many other domains, where humans and artificial intelligent systems cooperate. One of the main issues arising in this context regards ways to model and reason with moral values. In this paper we discuss the possible use of AI compact preference models as a promising approach to model, reason, and embed moral values in decision support systems.

Context-recognition and activity recognition systems in multi-user environments such as smart homes, usually assume to know the number of occupants in the environment. However, being able to count the number of users in the environment is important in order to accurately recognize the activities of (groups of) agents. For smart environments without cameras, the problem of counting the number of agents is non-trivial. This is in part due to the difficulty of using a single non-vision based sensors to discriminate between one or several persons, and thus information from several sensors must be combined in order to reason about the presence of several agents. In this paper we address the problem of counting the number of agents in a topologically known environment using simple sensors that can indicate anonymous human presence. To do so, we connect an ontology to a probabilistic model (a Hidden Markov Model) in order to estimate the number of agents in each section of the environment. We evaluate our methods on a smart home setup where a number of motion and pressure sensors are distributed in various rooms of the home.

We present an agent-based model of manipulating prices in financial markets through spoofing: submitting spurious orders to mislead other traders. Built around the standard limit-order mechanism, our model captures a complex market environment with combined private and common values, the latter represented by noisy observations of a fundamental time series. We start with zero intelligence traders, who ignore the order book, and introduce a version of heuristic belief learning (HBL) strategy that exploits the order book to predict price outcomes. By employing an empirical game-theoretic analysis to derive approximate strategic equilibria, we demonstrate the effectiveness of HBL and the usefulness of order book information in a range of non-spoofing environments. We further show that a market with HBL traders is spoofable, in that a spoofer can qualitatively manipulate prices towards its desired direction. After re-equilibrating games with spoofing, we find spoofing generally hurts market surplus and decreases the proportion of HBL. However, HBL's persistence in most environments with spoofing indicates a consistently spoofable market. Our model provides a way to quantify the effect of spoofing on trading behavior and efficiency, and thus measures the profitability and cost of an important form of market manipulation.

From Young 2010), frames (Zhu and Ontañón 2014), plotpoints early games like Zork, to the text-based interactive Victorian (Weyhrauch and Bates 1997; Nelson and Mateas 2005; dramas generated by Versu (Evans and Short 2014) Sharma et al. 2010) or social models (McCoy et al. 2011), to 3D RPG games like Skyrim (Ruch 2011), the quality of the problem of how to computationally model narratives the stories play a crucial role in engaging the player and and story spaces remains open.

Unlike perfect-information games, imperfect-information games cannot be decomposed into subgames that are solved independently. Thus more computationally intensive equilibrium-finding techniques are used, and abstraction---in which a smaller version of the game is generated and solved---is essential. Endgame solving is the process of computing a (presumably) better strategy for just an endgame than what can be computationally afforded for the full game. Endgame solving has many benefits, such as being able to 1) solve the endgame in a finer information abstraction than what is computationally feasible for the full game, and 2) incorporate into the endgame actions that an opponent took that were not included in the action abstraction used to solve the full game. We introduce an endgame solving technique that outperforms prior methods both in theory and practice. We also show how to adapt it, and past endgame-solving techniques, to respond to opponent actions that are outside the original action abstraction; this significantly outperforms the state-of-the-art approach, action translation. Finally, we show that endgame solving can be repeated as the game progresses down the tree, leading to significantly lower exploitability. All of the techniques are evaluated in terms of exploitability; to our knowledge, this is the first time that exploitability of endgame-solving techniques has been measured in large imperfect-information games.

A kidney exchange is a centrally-administered barter market where patients swap their willing yet incompatible donors. Modern kidney exchanges use 2-cycles, 3-cycles, and chains initiated by non-directed donors (altruists who are willing to give a kidney to anyone) as the means for swapping. We propose significant generalizations to kidney exchange. We allow more than one donor to donate in exchange for their desired patient receiving a kidney. We also allow for the possibility of a donor willing to donate if any of a number of patients receive kidneys. Furthermore, we combine these notions and generalize them.The generalization is to exchange among organ clubs, where a club is willing to donate organs outside the club if and only if the club receives organs from outside the club according to given specifications. Forms of organ clubs already exist — under an arrangement where one gets to be in the club as a potential recipient if one is willing to donate one's organs to the club upon death. Our approach can be used as an inter-club exchange mechanism that increases systemwide good (and can also be applied to live donation). In this paper we introduce these ideas, present the notion of operation frames that can be used to sequence the operations across batches, and present integer programming formulations for the market clearing problems for these new types of organ exchanges.

Human attitudes of objection, protest, and rebellion have undeniable potential to bring about social benefits, from social justice to healthy balance in relationships. At times, they can even be argued to be ethically obligatory. Conversely, AI rebellion is largely seen as a dangerous, destructive prospect. With the increase of interest in collaborative human/AI environments in which synthetic agents play social roles or, at least, exhibit behavior with social and ethical implications, we believe that AI rebellion could have benefits similar to those of its counterpart in humans. We introduce a framework meant to help categorize and design Rebel Agents, discuss their social and ethical implications, and assess their potential benefits and the risks they may pose. We also present AI rebellion scenarios in two considerably different contexts (military unmanned vehicles and computational social creativity) that exemplify components of the framework.

Learning approximate payoff functions is important to understand the dynamics in multi-player interactions. In general repeat games, each player's payoff can be represented as a combination of all other players' action choices using normal forms, which grow exponentially as the number of action choices increases. Graphical games, however, provide a compact representation to specify the inter-relations where one player's action choice is influenced by its neighbourhood. In this paper, we present how to learn players' approximate payoff functions from normal-form representations, yet also learn a compact graphical game representation of the inter-relations among the players. In this normal form representation, we explore the structural connections of mutual influence between players' action choices in game playing. We formally describe the problem of learning a player influence network and give a novel reward structure-learning algorithm for multiagent graphical games, called the Multi-Descendent Regression Learning Structure Algorithm (MDRLSA). We evaluate MDRLSA on random graphical games generated in GAMUT. Experiments show that MDRLSA can efficiently identify the independence among players and extract the influence graph accurately. The running time of MDRLSA increases linearly with the number of strategy profiles of a game. Compared with state-of-the-art graphical game model learning methods, MDRLSA shows efficiency in terms of time and accuracy.

This work addresses the problem of choosing an appropriate plan for achieving a goal in any realistic complex situation where an agent has to respond and act upon uncertain and/or an unknown information. We use the belief-desire-intention (BDI) model, a popular model for developing agents. The flexibility of choosing among different plans to achieve a desired goal is one of the benefits of this model. This paper describes a particular algorithm for selecting the most appropriate plan. Since the agent may have to reason with incomplete or uncertain information, we explore how to integrate probabilities in the agent model for taking an appropriate action and keeping the system behavior within acceptable boundaries and compliance to acceptable norms. Considering the uncertainty of the current state of the environment, this process relies on probability and utility theory. The plan selection algorithm has been implemented with Jadex