Renz, Jochen (The Australian National University) | Ge, Xiaoyu (The Australian National University) | Gould, Stephen (The Australian National University) | Zhang, Peng (The Australian National University)
The aim of the Angry Birds AI competition (AIBIRDS) is to build intelligent agents that can play new Angry Birds levels better than the best human players. This is surprisingly difficult for AI as it requires similar capabilities to what intelligent systems need for successfully interacting with the physical world, one of the grand challenges of AI. As such the competition offers a simplified and controlled environment for developing and testing the necessary AI technologies, a seamless integration of computer vision, machine learning, knowledge representation and reasoning, reasoning under uncertainty, planning, and heuristic search, among others. Over the past three years there have been significant improvements, but we are still a long way from reaching the ultimate aim and, thus, there are great opportunities for participants in this competition.
Zavala, Laura (Medgar Evers College, City University of New York) | Murukannaiah, Pradeep K. (North Carolina State University) | Poosamani, Nithyananthan (North Carolina State University.) | Finin, Tim (University of Maryland, Baltimore County) | Joshi, Anupam (University of Maryland, Baltimore County) | Rhee, Injong (North Carolina State University, Raleigh) | Singh, Munindar P. (North Carolina State University)
The Platys project focuses on developing a high-level, semantic notion of location called place. A place, unlike a geospatial position, derives its meaning from a user’s actions and interactions in addition to the physical location where they occur. Our aim is to enable the construction of a large variety of applications that take advantage of place to render relevant content and functionality and thus, improve user experience. We consider elements of context that are particularly related to mobile computing. The main problems we have addressed to realize our place-oriented mobile computing vision, are representing places, recognizing places, engineering place-aware applications. We describe the approaches we have developed for addressing these problems and related subproblems. A key element of our work is the use of collaborative information sharing where users’ devices share and integrate knowledge about places. Our place ontology facilitates such collaboration. Declarative privacy policies allow users to specify contextual features under which they prefer to share or not share their information.
For humans and automation to effectively collaborate and perform tasks, all participants need access to a common representation of potentially relevant situational information, or context. This article describes a general framework for building context-aware interactive intelligent systems that comprises three major functions: (1) capture human-system interactions and infer implicit context; (2) analyze and predict user intent and goals; and (3) provide effective augmentation or mitigation strategies to improve performance, such as delivering timely, personalized information and recommendations, adjusting levels of automation, or adapting visualizations. Our goal is to develop an approach that enables humans to interact with automation more intuitively and naturally that is reusable across domains by modeling context and algorithms at a higher-level of abstraction. We first provide an operational definition of context and discuss challenges and opportunities for exploiting context. We then describe our current work towards a general platform that supports developing context-aware applications in a variety of domains. We then explore an example use case illustrating how our framework can facilitate personalized collaboration within an information management and decision support tool. Future work includes evaluating our framework.
Knowledge workers perform work on many tasks per day and often switch between tasks. When performing work on a task, a knowledge worker must typically search, navigate and dig through file systems, documents and emails, all of which introduce friction into the flow of work. This friction can be reduced, and productivity improved, by capturing and modeling the context of a knowledge worker’s task based on how the knowledge worker interacts with an information space. Captured task contexts can be used to facilitate switching between tasks, to focus a user interface on just the information needed by a task and to recommend potentially other useful information. We report on the use of task contexts and the effect of context on productivity for a particular kind of knowledge worker, software developers. We also report on qualitative findings of the use of task contexts by a more general population of knowledge workers.
Modern multicore computers provide an opportunity to parallelize plan recognition algorithms to decrease runtime. Viewing plan recognition as parsing based on a complete breadth first search, makes ELEXIR (engine for lexicalized intent recognition) (Geib 2009; Geib and Goldman 2011) particularly suited for parallelization. This article documents the extension of ELEXIR to utilize such modern computing platforms. We will discuss multiple possible algorithms for distributing work between parallel threads and the associated performance wins. We will show, that the best of these algorithms provides close to linear speedup (up to a maximum number of processors), and that features of the problem domain have an impact on the achieved speedup.
This article presents new algorithms for inferring users’ activities in a class of flexible and open-ended educational software called exploratory learning environments (ELE). Such settings provide a rich educational environment for students, but challenge teachers to keep track of students’ progress and to assess their performance. This article presents techniques for recognizing students activities in ELEs and visualizing these activities to students. It describes a new plan recognition algorithm that takes into account repetition and interleaving of activities. This algorithm was evaluated empirically using two ELEs for teaching chemistry and statistics used by thousands of students in several countries. It was able to outperform the state-of-the-art plan recognition algorithms when compared to a gold-standard that was obtained by a domain-expert. We also show that visualizing students’ plans improves their performance on new problems when compared to an alternative visualization that consists of a step-by-step list of actions.
We discuss the nature of big data and address the role of semantics in analyzing and processing big data that arises in the context of physical-cyber-social systems. To handle volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision-making. To handle variety, we resort to semantic models and annotations of data so that intelligent processing can be done independent of heterogeneity of data formats and media. To handle velocity, we seek to use continuous semantics capability to dynamically create event or situation specific models and recognize relevant new concepts, entities and facts. To handle veracity, we explore trust models and approaches to glean trustworthiness. These four v's of big data are harnessed by the semantics-empowered analytics to derive value to support applications transcending physical-cyber-social continuum.
There is a great deal of interest in big data, focusing mostly on dataset size. An equally important dimension of big data is variety, where the focus is to process highly heterogeneous datasets. We describe how we use semantics to address the problem of big data variety. We also describe Karma, a system that implements our approach and show how Karma can be applied to integrate data in the cultural heritage domain. In this use case, Karma integrates data across many museums even though the datasets from different museums are highly heterogeneous.
Big data is having a disruptive impact across the sciences. Human annotation of semantic interpretation tasks is a critical part of big data semantics, but it is based on an antiquated ideal of a single correct truth that needs to be similarly disrupted. We expose seven myths about human annotation, most of which derive from that antiquated ideal of truth, and dispell these myths with examples from our research. We propose a new theory of truth, crowd truth, that is based on the intuition that human interpretation is subjective, and that measuring annotations on the same objects of interpretation (in our examples, sentences) across a crowd will provide a useful representation of their subjectivity and the range of reasonable interpretations.