Victor Hung

Soar Technology, Inc. · Intelligent Training

The tedious, often hand-modeled, activity of designing and implementing simulation scenarios can benefit from modern-day data-driven methods, i.e., machine-learning (ML). We envision a toolchain that exploits information obtained during live operations, such as the observed maneuvers, techniques, and procedures of all interacting players in live op...

This article describes a knowledge preservation and re-use tool designed to capture the knowledge of a specific individual at the US National Science Foundation, for later retrieval by successors after his retirement. The system is designed in a Q&A format, where it is sufficiently intelligent to ask for clarifying questions. The primary objective...

Early Natural Language Processing (NLP) endeavors often employed contextual cues as supplemental assistive measures—secondary sources of data to help understand its users’ linguistic inputs. Context was used more as a tie-breaking tool rather than as a central component in conversational negotiation. Recent work in context-based reasoning has inspi...

This paper describes research that addresses the problem of dialog management from a strong, context-centric approach. We further present a quantitative method of measuring the importance of contextual cues when dealing with speech-based human–computer interactions. It is generally accepted that using context in conjunction with a human input, such...

This article describes research to build an embodied conversational agent (ECA) as an interface to a question-and-answer (Q/A) system about a National Science Foundation (NSF) program. We call this ECA the LifeLike Avatar, and it can interact with its users in spoken natural language to answer general as well as specific questions about specific to...

This paper presents the feasibility of implementing an instinct-based behavioral model in an artificial agent. This research serves as a collection of information related to devising an artificially intelligent entity whose decisions are based on human instinct. The treatment of instincts will be defined by the bounds of psychology and ethnology. T...

Wireless sensor networks (WSNs) have become increasingly appealing in recent years for the purpose of data acquisition, surveillance, event monitoring, etc. Optimal positioning of wireless sensor nodes is an important issue for small networks of relatively expensive sensing devices. For such networks, the placement problem requires that multiple ob...

The evaluation of conversational dialog systems has remained a controversial topic, as it is challenging to quantitatively assess how well a conversation agent performs, or how much better one is compared to another. Furthermore, one of the hurdles which remains elusive in this quandary is the definition of naturalness, as demonstrated by how well...

Speech-based conversation agents describe those computer-based entities that interact with humans to help accomplish a certain task via spoken word input. This paper proposes a method of managing spoken dialog interactions in response to recognizing the human user's goals when accessing an expert system. In particular, a set of goals can co-exist d...

The development of avatars has significant potential to enhance realism, automation capability, and effectiveness across a variety of training environments. Project Lifelike is a three-year National Science Foundation effort whose objective is to develop and evaluate realistic avatar interfaces as portals to intelligent programs capable of relaying...

In this paper, an evolutionary algorithm is presented to develop a solution for the variable radii sensor placement optimization problem. Sensor nodes are placed in a sensor field such that maximum coverage of the target region is achieved. Coverage is defined in terms of monitoring points of interest. In point coverage, it is desired to place the...

Speech-based training agents can be described as virtual humans posing as interactive training characters with the capability to communicate in a spoken conversational manner. While creating this technology, developers face two stumbling blocks: 1) modeling the agent and its training knowledge is a time-consuming and tedious task, and 2) modern spe...