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.

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.

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.

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.

This paper describes a new candidate method of computing autonomous "self confidence." We describe how to analyze a plan for possible but unexpected break down cases and how to adapt the plan to circumvent those conditions. We view the result plan as more stable than the original one. The ability of achieving such plan stability is the core of how we propose to compute a system’s self confidence in its decisions and plans. This paper summarizes this approach and presents a preliminary evaluation that shows our approach is promising.

This paper presents a model of exaggeration suitable for implementation on a robot. Exaggeration is an interest form of dishonesty in that it serves as a tradeoff between the different costs associated with lying and the reward received by having one’s lie accepted. Moreover, exaggeration offers the deceiver additional control in the form of much the exaggerated statement differs from the truth. We use a color guessing game to examine the different tradeoffs between these costs and rewards and their impact on exaggeration. Our results indicate some amount of exaggeration is the preferred option during most early interactions. Further, because the cost of lying increases linear with the number of lies, exaggeration decreases with additional interactions. We conclude by arguing why social robots must be capable of lying.

In an earlier paper, I described in some detail how a system based on symbolic knowledge representation and reasoning could model and reason about an act of deception encountered in a children's story. This short position paper extends that earlier work, adding new analysis and discussion about the nature of deception, the desirability of building deceptive AI systems, and the computational mechanisms necessary for deceiving others and for recognizing their attempts to deceive us.

In a symposium focused on deception and counter-deception in machines, one might be immediately drawn to a narrow conception of those phenomena which highlight the pernicious ways in which they might be used. On the broader notion of fibbing that we describe in our talk, the social function of being fast and loose with the truth takes center stage as a tool for accomplishing a wide variety of socially centered goals. We briefly review the FIDE framework, described in (Isaac & Bridewell 2014; Bridewell & Bello 2014), including the conceptual resources it requires and the variety of fib-related concepts it supports. FIDE delineates between the aforementioned concepts as ends, and the strategic means by which the fibber might achieve these ends. In doing so, we show that certain types of difficult to conceptualize behavior, most notably bullshitting (Frankfurt 2006) and responses to bullshitting, are instances of a kind of strategy for impression management that serves higher-order social goals.

Intelligent systems rely on pattern recognition and signature-based approaches for a wide range of sensors enhancing situational awareness. For example, autonomous systems depend on environmental sensors to perform their tasks and secure systems depend on anomaly detection methods. The availability of large amount of data requires the processing of data in a “streaming” fashion with online algorithms. Yet, just as online learning can enhance adaptability to a non-stationary environment, it introduces vulnerabilities that can be manipulated by adversaries to achieve their goals while evading detection. Although human intelligence might have evolved from social interactions, machine intelligence has evolved as a human intelligence artifact and been kept isolated to avoid ethical dilemmas. As our adversaries become sophisticated, it might be time to revisit this question and examine how we can combine online learning and reasoning leading to the science of deceptive and counter-deceptive machines.