Guesgen, Hans W.



The AAAI-13 Conference Workshops

AI Magazine

The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14–15, 2013 at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context-Aware System Architectures (WS-13-05); Artificial Intelligence and Robotics Methods in Computational Biology (WS-13-06); Combining Constraint Solving with Mining and Learning (WS-13-07); Computer Poker and Imperfect Information (WS-13-08); Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS-13-09); Intelligent Robotic Systems (WS-13-10); Intelligent Techniques for Web Personalization and Recommendation (WS-13-11); Learning Rich Representations from Low-Level Sensors (WS-13-12); Plan, Activity, and Intent Recognition (WS-13-13); Space, Time, and Ambient Intelligence (WS-13-14); Trading Agent Design and Analysis (WS-13-15); and Statistical Relational Artificial Intelligence (WS-13-16).


An Approach to Numeric Refinement in Description Logic Learning for Learning Activities Duration in Smart Homes

AAAI Conferences

In spatio-temporal reasoning, granularity is one of the factors to be considered when aiming at an effective and efficient representation of space and time. There is a large body of work which addresses the issue of granularity by representing space and time on a qualitative level. Other approaches use a predefined scale which implicitly determines granularity (e.g., seconds, minutes, hours, days, month, etc.). However, there are situations where the right level of granularity is unknown in the beginning, and is only determined in the problem solving process itself. This is the case in machine learning, where the learner has to find a representation for a problem and with that the right granularity for representing space and time. This paper introduces an algorithm which determines the most appropriate level of granularity during training. It uses several description logic learners as the learners, and the positive and negative examples presented to them as the determinators for refining coarse temporal representations to the most appropriate level of granularity.


Preface

AAAI Conferences

This workshop has a special focus on the topic of spatio-temporal aspects of human activity interpretation, especially welcoming research concerned with monitoring and inter- pretation of people interactions, real-time commonsense situational awareness involving aspects such as scene perception and understanding, perceptual data analytics, and prediction and explanation-driven high-level control of autonomous systems. In this context, basic topics deemed important include activity and process models; behaviour and intention interpretation; spatial learning; modeling and reasoning about space, events, actions, interaction; spatio-temporal dynamics; and commonsense reasoning about spatio-temporal change.


Solving the Traveling Tournament Problem with Iterative-Deepening A*

AAAI Conferences

We give an overview of our journal paper on applying iterative-deepening A* to the traveling tournament problem, a combinatorial optimization problem from the sports scheduling literature. This approach involved combining past ideas and creating new ideas to help reduce node expansion. This resulted in a state-of-the-art approach for optimally solving instances of the traveling tournament problem. It was the first approach to solve the classic NL10 and CIRC10 instances, which had not been solved since the problem’s introduction.


Unsupervised Learning of Human Behaviours

AAAI Conferences

Behaviour recognition is the process of inferring the behaviour of an individual from a series of observations acquired from sensors such as in a smart home. The majority of existing behaviour recognition systems are based on supervised learning algorithms, which means that training them requires a preprocessed, annotated dataset. Unfortunately, annotating a dataset is a rather tedious process and one that is prone to error. In this paper we suggest a way to identify structure in the data based on text compression and the edit distance between words, without any prior labelling. We demonstrate that by using this method we can automatically identify patterns and segment the data into patterns that correspond to human behaviours. To evaluate the effectiveness of our proposed method, we use a dataset from a smart home and compare the labels produced by our approach with the labels assigned by a human to the activities in the dataset. We find that the results are promising and show significant improvement in the recognition accuracy over Self-Organising Maps (SOMs).


Report on the Twenty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS-23)

AI Magazine

The 23rd International Florida Artificial Intelligence Research Society Conference (FLAIRS-23) was held May 19-21, 2010 at The Shores Resort & Spa in Daytona Beach Shores, Florida, USA. The conference featured an exciting lineup of invited speakers, a general conference track on artificial intelligence research, and numerous special tracks. The conference chair was David Wilson from the University of North Carolina at Charlotte. The special tracks coordinator was Philip McCarthy from the University of Memphis.


Report on the Twenty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS-23)

AI Magazine

The 23rd International Florida Artificial Intelligence Research Society Conference (FLAIRS-23) was held May 19-21, 2010 at The Shores Resort & Spa in Daytona Beach Shores, Florida, USA. The conference featured an exciting lineup of invited speakers, a general conference track on artificial intelligence research, and numerous special tracks. The conference chair was David Wilson from the University of North Carolina at Charlotte. The program co-chairs were R. Charles Murray from Carnegie Learning and Hans W. Guesgen from Massey University in New Zealand. The special tracks coordinator was Philip McCarthy from the University of Memphis.