This paper investigates how spatial and temporal context informationcan be used in smart homes to detect abnormal behaviours.We discuss how various formalisms, such as probabilitytheory, the Dempster-Shafer calculus, and fuzzy logic,can be used to capture context information and argue thatfuzzy logic is the most suitable. We evaluate our approachby analysing one of the CASAS smart home datasets.

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.

Benjamin Grosof (Coherent Knowledge from episodic memory to great progress is being made on methods Systems) on representing activity create semantic memory, using a combination to solve problems related to structure context through semantic rule methods, of semantic memory and prediction, motion simulation, deriving from experience in the episodic memory to guide users?

Evolutionary Algorithms (EA) are a branch of heuristic population-based optimization tools that is growing in popularity (especially for combinatorial and other problems with poorly understood landscapes). Despite their many uses, there are no proofs that an EA will always converge to the global optimum for any general problem.

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).

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.

The Best Paper award went to Sidney D'Mello, Blair Lehman, and Natalie Person for "Expert Tutors' Feedback Is Immediate, Direct, and Discriminating" in the special track on Intelligent Tutoring Systems. The Best Student Paper award went to Rong Hu, Brian Mac Namee, and Sarah Jane Delany for "Off to a Good Start: Using Clustering to Select the Initial Training Set in Active Learning" in the general conference. The Best Poster award went to Robert Holder for "Problem Space Analysis for Library Generation and Algorithm Selection in Real-Time Systems" in the general conference. In addition to a diverse assortment of papers and British Columbia, who presented "What Should posters presented at the conference, FLAIRS-23 featured the World-Wide Mind Believe? Information about FLAIRS-24, University, who presented "Rational Ways of Talking"; including the call for papers, is available online at and Janet L. Kolodner of the Georgia Institute www.flairs-24.info. of Technology, who presented "How Can We Help Université de Paris-Sorbonne, who presented "Reasoning in Natural Language Using Combinatory Games"; and David Poole of the University of

The correct recognition of behaviours based on sensor observations in a smart home is a challenging problem; the sensor observations themselves can be noisy, and the pattern activity seen for a behaviour is rarely identical for different occurrences of the behaviour. For this reason, probabilistic methods such as Hidden Markov Models are preferred over symbolic reasoning approaches. However, these models do not deal well with interleaved behaviours, nor do they allow small variations in behaviour to be detected as abnormal, although this might be useful for the smart home, since changes in ingrained habit could be early signs of illness. We propose methods for using Allen's temporal relations in order to solve these problems, and demonstrate how they can be used to recognise the interleaving of different behaviours, as well as to reason about behaviours that are frequently seen together, and therefore form a behavioural pattern or habit. In this way we have been able to extend our behaviour recognition system to recognise unusual presentations of behaviours.

The 22nd International Florida Artificial Intelligence Research Society Conference (FLAIRS-22) was held 19th – 21st May 2009 at the Sundial Beach and Golf Resort on Sanibel Island, Florida, USA. It continued a long tradition of FLAIRS conferences, which attract researchers from around the world. The conference featured technical papers, special tracks, and invited speakers. The special tracks were coordinated by Philip McCarthy, from the University of Memphis.

The 22nd International Florida Artificial Intelligence Research Society Conference (FLAIRS-22) was held 19th – 21st May 2009 at the Sundial Beach and Golf Resort on Sanibel Island, Florida, USA.  It continued a long tradition of FLAIRS conferences, which attract researchers from around the world.  The conference featured technical papers, special tracks, and invited speakers.  This year’s conference was chaired by Susan Haller, from the State University of New York at Potsdam.  Conference program co-chairs were Hans W. Guesgen, from Massey University in New Zealand, and H. Chad Lane, from the University of Southern California.  The special tracks were coordinated by Philip McCarthy, from the University of Memphis.