Although techniques for finding Nash equilibria in extensive form games have become more powerful in recent years, many games that model real world interactions remain too large to be solved directly. The current approach is to create a smaller abstracted game, allowing the computation of an optimal solution. The strategy can then be used in the original game. Considering public information to create the abstraction can be strategically important, yet very few of the previous abstraction algorithms specifically consider public information or use an expert approach. In this paper, we show that the public information can be crucial, and we present a new, automatic technique for abstracting the public state space. We also present an experimental evaluation in the domain of Texas Hold’em poker and show that it outperforms state-of-the-art abstraction algorithms.

A unique behavior of humans is modifying one’s unobservable behavior based on the reaction of others for cooperation. We used a card game called Hanabi as an evaluation task of imitating human reflective intelligence with artificial intelligence. Hanabi is a cooperative card game with incomplete information. A player cooperates with an opponent in building several card sets constructed with the same color and ordered numbers. However, like a blind man's bluff, each player sees the cards of all other players except his/her own. Also, communication between players is restricted to information about the same numbers and colors, and the player is required to read his/his opponent's intention with the opponent's hand, estimate his/her cards with incomplete information, and play one of them for building a set. We compared human play with several simulated strategies. The results indicate that the strategy with feedbacks from simulated opponent's viewpoints achieves more score than other strategies.

Reducing an automobile's energy consumption will lower its dependency on fossil fuel and extend the travel range of electric vehicles. Automobile Climate Control Systems (CCS) are known to be heavy energy consumers. To help reduce CCS energy consumption, this paper presents an adaptive automated agent, MDP Agent for Climate control Systems -- MACS, which provides drivers advice as to how to set their CCS. First, we present a model which has 78% accuracy in predicting drivers' reactions to different advice in different situations. Using the prediction model, we designed a Markov Decision Process which solution provided the advising policy for MACS. Through empirical evaluation using an electric car, with 83 human subjects, we show that MACS successfully reduced the energy consumption of the subjects by 33% compared to subjects who were not equipped with MACS. MACS also outperformed the state-of-the-art Social agent for Advice Provision (SAP).

In this paper, economic principles and the paradigm of a game are used to create a signal control strategy. The game structure is not formal (as in game theory), but the idea of a game is used nonetheless. That is, instead of using the standard techniques of minimum greens, maximum greens, and gaps to control the signal indications, an economically based game structure is employed. The intersection’s space is viewed as a scarce commodity whose use is determined through a bidding process. Movement Managers manage the vehicle departures for specific turning movements. Arriving motorists pay the Movement Managers an initial fee, and make voluntary contributions as they perceive necessary to arrange times of entry for them. Movement Managers submit bids for use of the intersection’s space and the highest bidders win. Distributed processing and connected vehicle technology are seen as the mechanisms by which implementation would be feasible. The value in such an idea is that one can study and reach an understanding of the economics that underlie effective traffic control.

We propose and evaluate a number of solutions to the problem of calculating the cost to serve each location in a single-vehicle transport setting. Such cost to serve analysis has application both strategically and operationally in transportation. The problem is formally given by the traveling salesperson game (TSG), a cooperative total utility game in which agents correspond to locations in a travelling salesperson problem (TSP). The cost to serve a location is an allocated portion of the cost of an optimal tour. The Shapley value is one of the most important normative division schemes in cooperative games, giving a principled and fair allocation both for the TSG and more generally. We consider a number of direct and sampling-based procedures for calculating the Shapley value, and present the first proof that approximating the Shapley value of the TSG within a constant factor is NP-hard. Treating the Shapley value as an ideal baseline allocation, we then develop six proxies for that value which are relatively easy to compute. We perform an experimental evaluation using Synthetic Euclidean games as well as games derived from real-world tours calculated for fast-moving consumer goods scenarios. Our experiments show that several computationally tractable allocation techniques correspond to good proxies for the Shapley value.

Assuming computational technologies as a dominant factor in forming new scientific methods during the last century, we review the field of computational urban modeling based on the ways different approaches deal with evolving computational and informational capacities. We claim that during the last few years, due to advancements in ubiquitous computing the flow of unstructured data streams have changed the landscape of empirical modeling and simulation. However, there is a conceptual mismatch between the state of the art in urban modeling paradigms and the capacities offered by these urban data streams. We discuss some alternative mathematical methodologies that introduce an abstraction from the traditional urban modeling methodologies.

Energy consumption in commercial and educational buildings is impacted by group activities such as meetings, workshops, classes and exams, and can be reduced by scheduling these activities to take place at times and locations that are favorable from an energy standpoint. This paper improves on the effectiveness of energy-aware room-booking and occupancy scheduling approaches, by allowing the scheduling decisions to rely on an explicit model of the building's occupancy-based HVAC control. The core component of our approach is a mixed-integer linear programming (MILP) model which optimally solves the joint occupancy scheduling and occupancy-based HVAC control problem. To scale up to realistic problem sizes, we embed this MILP model into a large neighbourhood search (LNS). We obtain substantial energy reduction in comparison with occupancy-based HVAC control using arbitrary schedules or using schedules obtained by existing heuristic energy-aware scheduling approaches.

Hutter's universal artificial intelligence (AI) showed how to define future AI systems by mathematical equations. Here we adapt those equations to define a self-modeling framework, where AI systems learn models of their own calculations of future values. Hutter discussed the possibility that AI agents may maximize rewards by corrupting the source of rewards in the environment. Here we propose a way to avoid such corruption in the self-modeling framework. This paper fits in the context of my book Ethical Artificial Intelligence.

Given the fast rise of increasingly autonomous artificial agents and robots, a key acceptability criterion will be the possible moral implications of their actions. In particular, intelligent persuasive systems (systems designed to influence humans via communication) constitute a highly sensitive topic because of their intrinsically social nature. Still, ethical studies in this area are rare and tend to focus on the output of the required action. Instead, this work focuses on the persuasive acts themselves (e.g. “is it morally acceptable that a machine lies or appeals to the emotions of a person to persuade her, even if for a good end?”). Exploiting a behavioral approach, based on human assessment of moral dilemmas – i.e. without any prior assumption of underlying ethical theories – this paper reports on a set of experiments. These experiments address the type of persuader (human or machine), the strategies adopted (purely argumentative, appeal to positive emotions, appeal to negative emotions, lie) and the circumstances. Findings display no differences due to the agent, mild acceptability for persuasion and reveal that truth-conditional reasoning (i.e. argument validity) is a significant dimension affecting subjects’ judgment. Some implications for the design of intelligent persuasive systems are discussed.

Autonomy and agency are a central property in robotic systems, human-machine interfaces, e-business, ambient intelligence and assisted living applications. As the complexity of the situations the autonomous agents may encounter in such contexts is increasing, the decisions those agents make must integrate new issues, e.g. decisions involving contextual ethical considerations. Consequently contributions have proposed recommendations, advice or hard-wired ethical principles for systems of autonomous agents. However, socio-technical systems are more and more open and decentralized, and involve autonomous artificial agents interacting with other agents, human operators or users. For such systems, novel and original methods are needed to address contextual ethical decision-making, as decisions are likely to interfere with one another. This paper aims at presenting the ETHICAA project (Ethics and Autonomous Agents) whose objective is to define what should be an autonomous entity that could manage ethical conflicts. As a first proposal, we present various practical case studies of ethical conflicts and highlight what their main system and decision features are.