When to make a decision is a key question in decision making problems characterized by uncertainty. In this paper we deal with decision making in environments where information arrives dynamically. We address the tradeoff between waiting and stopping strategies. On the one hand, waiting to obtain more information reduces uncertainty, but it comes with a cost. Stopping and making a decision based on an expected utility reduces the cost of waiting, but the decision is based on uncertain information. We propose an optimal algorithm and two approximation algorithms. We prove that one approximation is optimistic - waits at least as long as the optimal algorithm, while the other is pessimistic - stops not later than the optimal algorithm. We evaluate our algorithms theoretically and empirically and show that the quality of the decision in both approximations is near-optimal and much faster than the optimal algorithm. Also, we can conclude from the experiments that the cost function is a key factor to chose the most effective algorithm.

This study focuses on the question: "What are the computational formalisms at the heart of deceptive and counter-deceptive machines?" We formulate deception using a dynamic epistemic logic. Three different types of deception are considered: deception by lying, deception by bluffing and deception by truth-telling, depending on whether a speaker believes what he/she says or not. Next we consider various situations where an act of deceiving happens. Intentional deception is accompanied by a speaker's intent to deceive. Indirect deception happens when false information is carried over from person to person. Self-deception is an act of deceiving the self. We investigate formal properties of different sorts of deception.

Autonomous mobile service robots move in our buildings, carrying out different tasks and traversing multiple floors. While moving and performing their tasks, these robots find themselves in a variety of states. Although speech is often used for communicating the robot’s state to humans, such communication can often be ineffective, due to the transient nature of speech. In this paper, we investigate the use of lights as a persistent visualization of the robot’s state in relation to both tasks and environmental factors. Programmable lights offer a large degree of choices in terms of animation pattern, color and speed. We present this space of choices and introduce different animation profiles that we consider to animate a set of programmable lights on the robot. We conduct experiments to query about suitable animations for three representative scenarios of an autonomous symbiotic service robot, CoBot. Our work enables CoBot to make its states persistently visible to the humans it interacts with.

Non-player characters in games generally lack believability and deep interactivity. The AI system Comme il Faut begins to tackle this by modeling social state and behaviors for game characters. The player initiates social exchanges and the dialog and outcome are generated and displayed in their entirety. In this paper we present a model called social prac-tices to extend Comme il Faut. Social practices increase the playability of social play by modeling social interactions at a more granular level and adding interactivity at each stage. This model also moves away from dialog trees to a more modular form of authoring to support the additional com-plexity.

This paper analyzes repeated multimarket contact with observation errors where two players operate in multiple markets simultaneously. Multimarket contact has received much attention from the literature of economics,management, and information systems. Despite vast empirical studies that examine whether multimarket contact fosters cooperation/collusion, little is theoretically known as to how players behave in an equilibrium when each player receives a noisy observation of other firms’ actions. This paper tackles an essentially realistic situation where the players do not share common information; each player may observe a different signal (private monitoring). Thus, players have difficulty in having a common understanding about which market their opponent should be punished in and when punishment should be started and ended. We first theoretically show that an extension of 1-period mutual punishment (1MP) for an arbitrary number of markets can be an equilibrium. Second, by applying a verification method, we identify a simple equilibrium strategy called "locally cautioning (LC)" that restores collusion after observation error or deviation. We then numerically reveal that LC significantly outperforms 1MP and achieves the highest degree of collusion.

This paper examines the importance of presenting simple, intuitive tasks when conducting microtasking on crowdsourcing platforms. Most crowdsourcing platforms allow the maker of a task to present any length of instructions to crowd workers who participate in their tasks. Our experiments show, however, most workers who participate in crowdsourcing microtasks do not read the instructions, even when they are very brief. To facilitate success in microtask design, we highlight the importance of making simple, easy to grasp tasks that do not rely on instructions for explanation.

Hence, they do not address mobile resource devices, it has now become practically feasible to enable constraints (e.g., energy, bandwidth) and also result in unnecessary people to share information about dynamic events (e.g., trees spam. On the other hand, multi-cast approaches randomly fallen on roads due to a storm, sudden truck breakdowns send the queries to some of the users to preserve mobile and unscheduled processions) in their current location. This resources, but they do not ensure the direction of queries strongly motivates facilitation of various kinds of locationdependent to the most relevant users.

We develop a flexible reward plan to elicit truthful predictive probability distribution over a set of uncertain events from workers.  In our reward plan, the principal can assign rewards for incorrect predictions according to her similarity between events.  In the spherical proper scoring rule, a worker's expected utility is represented as the inner product of her truthful predictive probability and her declared probability. We generalize the inner product by introducing a reward matrix that defines a reward for each prediction-outcome pair. We show that if the reward matrix is symmetric and positive definite, the spherical proper scoring rule guarantees the maximization of a worker's expected utility when she truthfully declares her prediction.

Plan synthesis often requires complete domain models and initial states as input. In many real world applications, it is difficult to build domain models and provide complete initial state beforehand. In this paper we propose to turn to the crowd for help before planning. We assume there are annotators available to provide information needed for building domain models and initial states. However, there might be a substantial amount of discrepancy within the inputs from the crowd. It is thus challenging to address the planning problem with possibly noisy information provided by the crowd. We address the problem by two phases. We first build a set of Human Intelligence Tasks (HITs), and collect values from the crowd. We then estimate the actual values of variables and feed the values to a planner to solve the problem.

A key challenge in participatory sensing systems has been the design of incentive mechanisms that motivate individuals to contribute data to consuming applications. Emerging trends in urban development and smart city planning indicate the use of citizen reports to gather insights and identify areas for transformation. Consumers of these reports (e.g. city agencies) typically associate non-uniform utility (or values) to different reports based on the spatio-temporal context of the reports. For example, a report indicating traffic congestion near an airport, in early morning hours, would tend to have much higher utility than a similar report from a sparse residential area. In such cases, the design of an incentive mechanism must motivate participants, via appropriate rewards (or payments), to provide higher utility reports when compared to less valued ones. The main challenge in designing such an incentive scheme is two-fold: (i) lack of prior knowledge of participants in terms of their availability (i.e. who are in the vicinity) and reporting behaviour (i.e. what are the rewards expected); and (ii) minimizing payments to the reporters while ensuring that the desired number of reports are collected. In this paper, we propose STOC-PISCES, an algorithm that guarantees a stochastic optimal solution in the generalized setting of an unknown set of participants, with non-deterministic availabilities and stochastically rational reporting behaviour. The superior performance of STOC-PISCES in experimental settings, based on real-world data, endorses its adoption as an incentive strategy in participatory sensing applications like smart city management.