Jhala, Arnav


ProcDefense — A Game Framework for Procedural Player Skill Training

AAAI Conferences

A challenge of game design is in providing affordances to players so that they can learn and improve their skills. Ad- vances in Procedural Content Generation (PCG) suggest this type of game content is a candidate for automatic creation. Some work in PCG has been successful in customizing game difficulty to achieve desired player experience; however, this often involves bringing the difficulty of the game to a level appropriate for the player’s current skills. Players desiring to improve their performance in a particular game may be will- ing to tolerate relatively higher levels of frustration and anx- iety than are targeted in experience-based approaches. As an initial step in this line of inquiry, we introduce ProcDefense, an action game with a modular difficulty control interface, as a platform for future inquiry into the effectiveness of differing PCG techniques for performance-training, dynamic difficulty adjustment.


Proceduralist Readings, Procedurally

AAAI Conferences

While generative approaches to game design offer great promise, systems can only reliably generate what they can “understand,” often limited to what can be handencoded by system authors. Proceduralist readings, a way of deriving meaning for games based on their underlying processes and interactions in conjunction with aesthetic and cultural cues, offer a novel, systematic approach to game understanding. We formalize proceduralist argumentation as a logic program that performs static reasoning over game specifications to derive higher-level meanings (e.g., deriving dynamics from mechanics), opening the door to broader and more culturally-situated game generation.


Policy Communication for Coordination with Unknown Teammates

AAAI Conferences

Within multiagent teams research, existing approaches commonly assume agents have perfect knowledge regarding the decision process guiding their teammates' actions. More recently, ad hoc teamwork was introduced to address situations where an agent must coordinate with a variety of potential teammates, including teammates with unknown behavior. This paper examines the communication of intentions for enhanced coordination between such agents. The proposed decision-theoretic approach examines the uncertainty within a model of an unfamiliar teammate, identifying policy information valuable to the collaborative effort. We characterize this capability through theoretical analysis of the computational requirements as well as empirical evaluation of a communicative agent coordinating with an unknown teammate in a variation of the multiagent pursuit domain.


Reports of the AAAI 2014 Conference Workshops

AI Magazine

The AAAI-14 Workshop program was held Sunday and Monday, July 27–28, 2012, at the Québec City Convention Centre in Québec, Canada. The AAAI-14 workshop program included fifteen workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Robotics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Cognitive Computing for Augmented Human Intelligence; Computer Poker and Imperfect Information; Discovery Informatics; Incentives and Trust in Electronic Communities; Intelligent Cinematography and Editing; Machine Learning for Interactive Systems: Bridging the Gap between Perception, Action and Communication; Modern Artificial Intelligence for Health Analytics; Multiagent Interaction without Prior Coordination; Multidisciplinary Workshop on Advances in Preference Handling; Semantic Cities -- Beyond Open Data to Models, Standards and Reasoning; Sequential Decision Making with Big Data; Statistical Relational AI; and The World Wide Web and Public Health Intelligence. This article presents short summaries of those events.


Reports of the AAAI 2014 Conference Workshops

AI Magazine

The AAAI-14 Workshop program was held Sunday and Monday, July 27–28, 2012, at the Québec City Convention Centre in Québec, Canada. Canada. The AAAI-14 workshop program included fifteen workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Robotics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Cognitive Computing for Augmented Human Intelligence; Computer Poker and Imperfect Information; Discovery Informatics; Incentives and Trust in Electronic Communities; Intelligent Cinematography and Editing; Machine Learning for Interactive Systems: Bridging the Gap between Perception, Action and Communication; Modern Artificial Intelligence for Health Analytics; Multiagent Interaction without Prior Coordination; Multidisciplinary Workshop on Advances in Preference Handling; Semantic Cities — Beyond Open Data to Models, Standards and Reasoning; Sequential Decision Making with Big Data; Statistical Relational AI; and The World Wide Web and Public Health Intelligence. This article presents short summaries of those events.


RAPID: A Belief Convergence Strategy for Collaborating with Inconsistent Agents

AAAI Conferences

Maintaining an accurate set of beliefs in a partially observable scenario, particularly with respect to other agents operating in the same space, is a vital aspect of multiagent planning. We analyze how the beliefs of an agent can be updated for fast adaptivity to changes in the behavior of an unknown teammate. The main contribution of this paper is the empirical evaluation of an agent cooperating with a teammate whose goals change periodically. We test our approach in a collaborative multiagent domain where identification of goals is necessary for successful completion. The belief revision technique we propose outperforms the traditional approach in a majority of test cases. Additionally, our results suggest the ability to approximate a higher level model by utilizing a belief distribution over a set of lower level behaviors, particularly when the belief update strategy identifies changes in the behavior in a responsive manner.


EduCam: Cinematic Vocabulary for Educational Videos

AAAI Conferences

Cinematography relies on a rich vocabulary of shots and framing parameters to communicate narrative event structures. This cinematic vocabulary stems from the physical capabilities of camera movement and uses aesthetic aspects to engage the audience and influence audience comprehension. At present, automatic physical camera control is limited in cinematic vocabulary. Virtual cameras, on the other hand, are capable of executing a variety of cinematic idioms. Specifically, shot composition, match cut, and establishing shot sequence are three common cinematic techniques often lacking in automated physical camera systems. This paper proposes an architecture called EduCam as a platform for employing virtual camera control techniques in the limited stochastic physical environment of lecture capturing. EduCam uses reactive algorithms for real time camera coverage with hierarchical constraint satisfaction techniques for offline editing.


Multi-Modal Analysis of Movies for Rhythm Extraction

AAAI Conferences

This paper motivates a multi-modal approach for analysis of aesthetic elements of films through integration of visual and auditory features. Prior work in characterizing aesthetic elements of film has predominantly focused on visual features. We present comparison of analysis from multiple modalities in a rhythm extraction task. For detection of important events based on a model of rhythm/tempo we compare analysis of visual features and auditory features. We introduce an audio tempo function that characterizes the pacing of a video segment. We compare this function with its visual pace counterpart. Preliminary results indicate that an integrated approach could reveal more semantic and aesthetic information from digital media. With combined information from the two signals, tasks such as automatic identification of important narrative events, can enable deeper analysis of large-scale video corpora.


Automating Camera Control in Games Using Gaze

AAAI Conferences

This paper introduces a work in progress method for automating camera control in games using gaze tracking. Building on prior work by Burelli et al, we provide characterization of features for the gaze tracker and test a gaze-assisted camera control module in a first-person shooter game. We report results from a pilot study on the viability of gaze- assisted camera control. We also discuss challenges and viability of gaze-assisted camera controller for games and interactive applications. Finally, we present ongoing and future work in this area.