By expressiveness we mean the ability to encode a desired set of two-valued interpretations over a given propositional vocabulary A using only atoms from A. We also compare ADFs' expressiveness with that of (the two-valued semantics of) abstract argumentation frameworks, normal logic programs and propositional logic. While the computational complexity of the two-valued model existence problem for all these languages is (almost) the same, we show that the languages form a neat hierarchy with respect to their expressiveness. We then demonstrate that this hierarchy collapses once we allow to introduce a linear number of new vocabulary elements. We finally also analyse and compare the representational succinctness of ADFs (for two-valued model semantics), that is, their capability to represent two-valued interpretation sets in a space-efficient manner.

Socially Assistive Robotics (SAR) is a growing field dedicated to developing models and algorithms that enable robots to help people achieve goals through social interaction (Feil-Seifer and Mataric 2005). Prior work in this field has focused on one-on-one interactions, but there is interest in extending this work to multi-party interactions. We contribute to the study of multi-party SAR by defining the role of moderator, an agent that is responsible for directing an interaction, but is not necessarily directly participating in the task. We present a computational formalization of the task of moderation as the process by which a goal-directed multi-party interaction is regulated via manipulation of interaction resources, including both physical resources, such as an object or a tool, and social resources, such as the conversational floor or participants' attention. Finally, we present preliminary results of an analysis of self-moderated multi-party human-human interaction that support several of the underlying assumptions of this formalization.

In interactive storytelling systems and other story-based computer games, a drama manager is a background agent that aims to bring about an enjoyable and coherent experience for the players. In this paper, we present a personalized drama manager that increases a player's expected enjoyment without removing player agency. Our personalized drama manager models a player's preference using data-driven techniques, predicts the probability the player transitioning to different story experiences, selects an objective experience that can maximize the player's expected enjoyment, and guides the player to the selected story experience. Human study results show that our drama manager can significantly increase players' enjoyment ratings in an interactive storytelling testbed, compared to drama managers in previous research.

Video-game designers often tessellate continuous 2-dimensional terrain into a grid of blocked and unblocked square cells. The three main ways to calculate short paths on such a grid are to determine truly shortest paths, shortest vertex paths and shortest grid paths, listed here in decreasing  order of computation time and increasing order of resulting path length. We show that, for both vertex and grid paths on both 4-neighbor and 8-neighbor grids, placing vertices at cell corners rather than at cell centers tends to result in shorter paths. We quantify the advantage of cell corners over cell centers theoretically with tight worst-case bounds on the ratios of path lengths, and empirically on a large set of benchmark test cases. We also quantify the advantage of 8-neighbor grids over 4-neighbor grids.

Domain models for sequential decision making typically represent abstract versions of real-world systems. In practice, such representations are compact, easy to maintain, and affort faster solution times. Unfortunately, as we show in this paper, simple ways of abstracting solvable real-world problems may lead to models whose solutions are incorrect with respect to the real-world problem. There is some evidence that such limitations have restricted the applicability of SDM technology in the real world, as is apparent in the case of task and motion planning in robotics. We show that the situation can be ameliorated by a combination of increased expressive power---for example, allowing angelic nondeterminism in action effects---and new kinds of algorithmic approaches designed to produce correct solutions from initially incorrect or non-Markovian abstract models.

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.

Massive state spaces are ubiquitous throughout planning and reinforcement learning (RL) domains: agents involved in furniture assembly, cooking automation and backgammon must grapple with problem formalisms that are much too expansive to solve by conventional tabular approaches. However, modern tabular planning and RL techniques bypass this difficulty by using propositional functions to transfer knowledge across states — both within and across problem instances — to solve for near optimal behaviors in very large state spaces. Here we present a means by which useful propositional functions can be inferred from observations of transition dynamics. Our approach is based upon distilling salient relational values between pairs of objects. We then use these learned propositional functions to free the RL algorithm deterministic object-oriented RMAX (DOORMAX) of its dependence on expert-provided propositional functions. We also empirically demonstrate high correspondence between these learned propositional functions and expert-provided propositional functions. Our novel DOORMAX algorithm performs at a level near that of classic DOORMAX.

The number of applications of service robotics in public spaces such as hospitals, museums and malls is a growing trend. Public spaces, however, provide several challenges to the robot, and specifically with its planning capabilities: they need to cope with a dynamic and uncertain environment and are subject to particular human-robot interaction constraints. A major challenge is the Joint Intention problem. When cooperating with humans, a persistent commitment to achieve a shared goal cannot be always assumed, since they have an unpredictable behavior and may be distracted in environments as dynamic and uncertain as public spaces, and even more so if the human agents are customers,visitors or bystanders. In order to address such issues in a decision-making context, we present a framework based on Hierarchical Factored POMDPs. We describe the general method for ensuring the Joint Intention between human and robot , the hierarchical structure and the Value Decomposition method adopted to build it.We also provide an example application scenario: an Escort Task in a shopping mall for guiding a customer towards a desired point of interest.

Probabilistic graphical models, such as Bayesian networks, are intuitive and theoretically sound tools for modeling uncertainty. A major problem with applying Bayesian networks in practice is that it is hard to judge whether a model fits well a case that it is supposed to solve. One way of expressing a possible dissonance between a model and a case is the {\em surprise index}, proposed by Habbema, which expresses the degree of surprise by the evidence given the model. While this measure reflects the intuition that the probability of a case should be judged in the context of a model, it is computationally intractable. In this paper, we propose an efficient way of approximating the surprise index.

Autonomous unmanned aerial systems (UAS) are envisioned to become increasingly utilized in commercial airspace. In order to be attractive for commercial applications, UAS are required to undergo a quick development cycle, ensure cost effectiveness and work reliably in changing environments. Learning based adaptive control systems have been proposed to meet these demands. These techniques promise more flexibility when compared with traditional linear control techniques. However, no consistent verification and validation (V&V) framework exists for adaptive controllers. The underlying purpose of the V&V processes in certifying control algorithms for aircraft is to build trust in a safety critical system. In the past, most adaptive control algorithms were solely designed to ensure stability of a model system and meet robustness requirements against selective uncertainties and disturbances. However, these assessments do not guarantee reliable performance of the real system required by the V&V process. The question arises how trust can be defined for learning based adaptive control algorithms. From our perspective, self-confidence of an adaptive flight controller will be an integral part of building trust in the system. The notion of self-confidence in the adaptive control context relates to the estimate of the adaptive controller in its capabilities to operate reliably, and its ability to foresee the need for taking action before undesired behaviors lead to a loss of the system. In this paper we present a pathway to a possible answer to the question of how self-confidence for adaptive controllers can be achieved. In particular, we elaborate how algorithms for diagnosis and prognosis can be integrated to help in this process.