Artificial Intelligence and Robotics Commons^™

An Autonomous Agent For Learning Spatiotemporal Models Of Human Daily Activities, Shan Gao, Ah-Hwee Tan

Research Collection School Of Computing and Information Systems

Activities of Daily Living (ADLs) refer to activities performed by individuals on a daily basis. As ADLs are indicatives of a person’s habits, lifestyle, and well being, learning the knowledge of people’s ADL routine has great values in the healthcare and consumer domains. In this paper, we propose an autonomous agent, named Agent for Spatia-Temporal Activity Pattern Modeling (ASTAPM), being able to learn spatial and temporal patterns of human ADLs. ASTAPM utilises a self-organizing neural network model named Spatiotemporal - Adaptive Resonance Theory (ST-ART). ST-ART is capable of integrating multimodal contextual information, involving the time and space, wherein the ADL …

Reinforcement Learning Framework For Modeling Spatial Sequential Decisions Under Uncertainty: (Extended Abstract), Truc Viet Le, Siyuan Liu, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We consider the problem of trajectory prediction, where a trajectory is an ordered sequence of location visits and corresponding timestamps. The problem arises when an agent makes sequential decisions to visit a set of spatial locations of interest. Each location bears a stochastic utility and the agent has a limited budget to spend. Given the agent's observed partial trajectory, our goal is to predict the remaining trajectory. We propose a solution framework to the problem considering both the uncertainty of utility and the budget constraint. We use reinforcement learning (RL) to model the underlying decision processes and inverse RL to …

Simultaneous Optimization And Sampling Of Agent Trajectories Over A Network, Hala Mostafa, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

We study the problem of optimizing the trajectories of agents moving over a network given their preferences over which nodes to visit subject to operational constraints on the network. In our running example, a theme park manager optimizes which attractions to include in a day-pass to maximize the pass’s appeal to visitors while keeping operational costs within budget. The first challenge in this combinatorial optimization problem is that it involves quantities (expected visit frequencies of each attraction) that cannot be expressed analytically, for which we use the Sample Average Approximation. The second challenge is that while sampling is typically done …

Approximate Inference Using Dc Programming For Collective Graphical Models, Duc Thien Nguyen, Akshat Kumar, Hoong Chuin Lau, Daniel Sheldon

Research Collection School Of Computing and Information Systems

Collective graphical models (CGMs) provide a framework for reasoning about a population of independent and identically distributed individuals when only noisy and aggregate observations are given. Previous approaches for inference in CGMs work on a junction-tree representation, thereby highly limiting their scalability. To remedy this, we show how the Bethe entropy approximation naturally arises for the inference problem in CGMs. We reformulate the resulting optimization problem as a difference-of-convex functions program that can capture different types of CGM noise models. Using the concave-convex procedure, we then develop a scalable message-passing algorithm. Empirically, our approach is highly scalable and accurate for …

Modeling Autobiographical Memory In Human-Like Autonomous Agents, Di Wang, Ah-Hwee Tan, Chunyan Miao

Research Collection School Of Computing and Information Systems

Although autobiographical memory is an important part of the human mind, there has been little effort on modeling autobiographical memory in autonomous agents. With the motivation of developing human-like intelligence, in this paper, we delineate our approach to enable an agent to maintain memories of its own and to wander in mind. Our model, named Autobiographical Memory-Adaptive Resonance Theory network (AM-ART), is designed to capture autobiographical memories, comprising pictorial snapshots of one’s life experiences together with the associated context, namely time, location, people, activity, and emotion. In terms of both network structure and dynamics, AM-ART coincides with the autobiographical memory …

Approximating The Performance Of A "Last Mile" Transportation System, Hai Wang, Amedeo Odoni

Research Collection School Of Computing and Information Systems

The Last Mile Problem (LMP) refers to the provision of travel service from the nearest public transportation node to a home or office. We study the supply side of this problem in a stochastic setting, with batch demands resulting from the arrival of groups of passengers who request last-mile service at urban rail stations or bus stops. Closedform approximations are derived for the performance of Last Mile Transportations Systems as a function of the fundamental design parameters of such systems. An initial set of results is obtained for the case in which a fleet of vehicles of unit capacity provides …

A Study Of Android Malware Detection Techniques And Machine Learning, Balaji Baskaran, Anca Ralescu

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

Android OS is one of the widely used mobile Operating Systems. The number of malicious applications and adwares are increasing constantly on par with the number of mobile devices. A great number of commercial signature based tools are available on the market which prevent to an extent the penetration and distribution of malicious applications. Numerous researches have been conducted which claims that traditional signature based detection system work well up to certain level and malware authors use numerous techniques to evade these tools. So given this state of affairs, there is an increasing need for an alternative, really tough malware …

Extended Pixel Representation For Image Segmentation, Deeptha Girish, Vineeta Singh, Anca Ralescu

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

We explore the use of extended pixel representation for color based image segmentation using the K-means clustering algorithm. Various extended pixel representations have been implemented in this paper and their results have been compared. By extending the representation of pixels an image is mapped to a higher dimensional space. Unlike other approaches, where data is mapped into an implicit features space of higher dimension (kernel methods), in the approach considered here, the higher dimensions are defined explicitly. Preliminary experimental results which illustrate the proposed approach are promising.

An Autonomic Computing System Based On A Rule-Based Policy Engine And Artificial Immune Systems, Rahmira Rufus, William Nick, Joseph Shelton, Albert Esterline

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

Autonomic computing systems arose from the notion that complex computing systems should have properties like those of the autonomic nervous system, which coordinates bodily functions and allows attention to be directed to more pressing needs. An autonomic system allows the system administrator to specify high-level policies, which the system maintains without administrator assistance. Policy enforcement can be done with a rule based system such as Jess (a java expert system shell). An autonomic system must be able to monitor itself, and this is often a limiting factor. We are developing an automatic system that has a policy engine and uses …

Towards The Development Of A Cyber Analysis & Advisement Tool (Caat) For Mitigating De-Anonymization Attacks, Siobahn Day, Henry Williams, Joseph Shelton, Gerry Dozier

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

We are seeing a rise in the number of Anonymous Social Networks (ASN) that claim to provide a sense of user anonymity. However, what many users of ASNs do not know that a person can be identified by their writing style.

In this paper, we provide an overview of a number of author concealment techniques, their impact on the semantic meaning of an author's original text, and introduce AuthorCAAT, an application for mitigating de-anonymization attacks. Our results show that iterative paraphrasing performs the best in terms of author concealment and performs well with respect to Latent Semantic Analysis.

Situations And Evidence For Identity Using Dempster-Shafer Theory, William Nick, Yenny Dominguez, Albert Esterline

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

We present a computational framework for identity based on Barwise and Devlin’s situation theory. We present an example with constellations of situations identifying an individual to create what we call id-situations, where id-actions are performed, along with supporting situations. We use Semantic Web standards to represent and reason about the situations in our example. We show how to represent the strength of the evidence, within the situations, as a measure of the support for judgments reached in the id-situation. To measure evidence of an identity from the supporting situations, we use the Dempster-Shafer theory of evidence. We enhance Dempster- Shafer …

Student Understanding And Engagement In A Class Employing Comps Computer Mediated Problem Solving: A First Look, Jung Hee Kim, Michael Glass, Taehee Kim, Kelvin Bryant, Angelica Willis, Ebonie Mcneil, Zachery Thomas

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

COMPS computer-mediated group discussion exercises are being added to a second-semester computer programming class. The class is a gateway for computer science and computer engineering students, where many students have difficulty succeeding well enough to proceed in their major. This paper reports on first results of surveys on student experience with the exercises. It also reports on the affective states observed in the discussions that are candidates for analysis of group functioning. As a step toward computer monitoring of the discussions, an experiment in using dialogue features to identify the gender of the participants is described.

A Tool For Staging Mixed-Initiative Dialogs, Joshua W. Buck, Saverio Perugini

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

We discuss and demonstrate a tool for prototyping dialog-based systems that, given a high-level specification of a human-computer dialog, stages the dialog for interactive use. The tool enables a dialog designer to evaluate a variety of dialogs without having to program each individual dialog, and serves as a proof-of-concept for our approach to mixed-initiative dialog modeling and implementation from a programming language-based perspective.

Keynote Talk 2: Social And Perceptual Fidelity Of Avatars And Autonomous Agents In Virtual Reality, Benjamin Kunz

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

Advances in display, computing and sensor technologies have led to a revival of interest and excitement surrounding immersive virtual reality. Here, on the cusp of the arrival of practical and affordable virtual reality technology, are open questions regarding the factors that contribute to compelling and immersive virtual worlds.

In order for virtual reality to be useful as a tool for use in training, education, communication, research, content-creation and entertainment, we must understand the degree to which the perception of the virtual environment and virtual characters resembles perception of the real world.

Relatedly, virtual reality's utility in these contexts demands evidence …

Exploring Web-Based Visual Interfaces For Searching Research Articles On Digital Library Systems, Maxwell Fowler, Chris Bellis, Chris Perry, Beomjin Kim

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

Previous studies that present information archived in digital libraries have used either document meta-data or document content. The current search mechanisms commonly return text-based results that were compiled from the meta-data without reflecting the underlying content. Visual analytics is a possible solution for improving searches by presenting a large amount of information, including document content alongside meta-data, in a limited screen space. This paper introduces a multi-tiered visual interface for searching research articles stored in Digital Library systems. The goals of this system are to allow users to find research papers about their interests in a large work space, to …

Fuzzy Algorithms: Applying Fuzzy Logic To The Golden Ratio Search To Find Solutions Faster, Stephany Coffman-Wolph

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

Applying the concept of fuzzy logic (an abstract version of Boolean logic) to well-known algorithms generates an abstract version (i.e., fuzzy algorithm) that often results in computational improvements. Precision may be reduced but counteracted by gaining computational efficiency. The trade-offs (e.g., small increase in space, loss of precision) for a variety of applications are deemed acceptable. The fuzzification of an algorithm can be accomplished using a simple three-step framework. Creating a new fuzzy algorithm goes beyond simply converting the data from raw data into fuzzy data by additionally converting the operators and concepts into their abstract equivalents. This paper demonstrates: …

The Webid Protocol Enhanced With Group Access, Biometrics, And Access Policies, Cory Sabol, William Nick, Maya Earl, Joseph Shelton, Albert Esterline

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

The WebID protocol solves the challenge of remembering usernames and passwords. We enhance this protocol in three ways. First, we give it the ability to manage groups of agents and control their access to resources on the Web. Second, we add support for biometric access control to enhance security. Finally, we add support for OWL-based policies that may be federated and result in flexible access control.

Real-Time Unsupervised Clustering, Gabriel Ferrer

MAICS: The Modern Artificial Intelligence and Cognitive Science Conference

In our research program, we are developing machine learning algorithms to enable a mobile robot to build a compact representation of its environment. This requires the processing of each new input to terminate in constant time. Existing machine learning algorithms are either incapable of meeting this constraint or deliver problematic results. In this paper, we describe a new algorithm for real-time unsupervised clustering, Bounded Self-Organizing Clustering. It executes in constant time for each input, and it produces clusterings that are significantly better than those created by the Self-Organizing Map, its closest competitor, on sensor data acquired from a physically embodied …

Equality And Hierarchy In Human-Robot Interaction, Kathryn E. Golden Miss, Kimberly Stowers

Human Factors and Applied Psychology Student Conference

No abstract provided.

Grounding Robot Motion In Natural Language And Visual Perception, Scott Alan Bronikowski

Open Access Dissertations

The current state of the art in military and first responder ground robots involves heavy physical and cognitive burdens on the human operator while taking little to no advantage of the potential autonomy of robotic technology. The robots currently in use are rugged remote-controlled vehicles. Their interaction modalities, usually utilizing a game controller connected to a computer, require a dedicated operator who has limited capacity for other tasks.

I present research which aims to ease these burdens by incorporating multiple modes of robotic sensing into a system which allows humans to interact with robots through a natural-language interface. I conduct …