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.

This case study article describes the iterative design process of an adaptive, mixed-initiative calendaring tool with embedded artificial intelligence.  We establish the specific types of assistance in which the target user population expressed interest, and we highlight our findings regarding the scheduling practices and the reminding preferences of these users.  These findings motivated the redesign and enhancement of our intelligent system.  Lessons learned from the study—namely, highlighting the merits of usability toward widespread adoption and retention, and that simple problems that perhaps do not necessitate complex AI-based solutions should not go unattended merely due to their inherent simplicity—conclude the article, along with a discussion of the importance of the iterative design process for any user adaptive system.

We report part of the design experience gained in X-Media, a system for knowledge management and sharing. Consolidated techniques of interaction design (scenario-based design) had to be revisited to capture the richness and complexity of intelligent interactive systems. We show that the design of intelligent systems requires methodologies (faceted scenarios) that support the investigation of intelligent features and usability factors simultaneously. Interaction designers become mediators between intelligent technology and users, and have to facilitate reciprocal understanding.

For text entry methods to be useful they have to deliver high entry rates and low error rates. At the same time they need to be easy-to-learn and provide effective means of correcting mistakes. Intelligent text entry methods combine AI techniques with HCI theory to enable users to enter text as efficiently and effortlessly as possible. Here I sample a selection of such techniques from the research literature and set them into their historical context. I then highlight five challenges for text entry methods that aspire to make an impact in our society: localization, error correction, editor support, feedback, and context of use.

The RADAR project developed a large multi-agent system with a mixed-initiative user interface designed to help office workers cope with email overload. Most RADAR agents observe experts performing tasks and then assist other users who are performing similar tasks. The interaction design for RADAR focused on developing user interfaces that allowed the intelligent functionality to improve the user’s workflow without frustrating the user when the system’s suggestions were either unhelpful or simply incorrect. For example with regards to autonomy, the RADAR agents were allowed much flexibility in selecting ways to assist the user, but were restricted from taking actions that would be visible to other people. This policy ensured that the user remained in control and mitigated the negative effects of mistakes. A large evaluation of RADAR demonstrated that novice users confronted with an email overload test performed significantly better, achieving a 37% better overall score when assisted by RADAR. The evaluation showed that AI technologies can help users accomplish their goals.

These unintended negative consequences of the introduction of intelligence often have no direct relationship with the intended benefits, just as the adverse effects of a medication may bear no obvious relationship to the intended benefits of taking that medicine. Therefore, these negative consequences can be seen as side effects. The purpose of this article is to give designers, developers, and users of interactive intelligent systems a detailed awareness of the potential side effects of AI. As with medications, awareness of the side effects can have different implications: We may be relieved to see that a given side effect is unlikely to occur in our particular case. We may become convinced that it will inevitably occur and therefore decide not to "take the medicine" (that is, decide to stick with mainstream systems). Or most likely and most constructively, by looking carefully at the causes of the side effects and the conditions under which they can occur, we can figure out how to exploit the benefits of AI in interactive systems while avoiding the side effects.

This is an opinion piece about the relationship between the fields of human-computer interaction (HCI), and artificial intelligence (AI). The ultimate goal of both fields is to make user interfaces more effective and easier to use for people. But historically, they have disagreed about whether "intelligence" or "direct manipulation" is the better route to achieving this. There is an unjustified perception in HCI that AI is unreliable. There is an unjustified perception in AI that interfaces are merely cosmetic. This disagreement is counterproductive.This article argues that AI's goals of intelligent interfaces would benefit enormously by the user-centered design and testing principles of HCI. It argues that HCI's stated goals of meeting the needs of users and interacting in natural ways, would be best served by application of AI. Peace.

When creating algorithms or systems that are supposed to be used by people, we should be able to adopt a “binocular” view of users’ interaction with intelligent systems: a view that regards the design of interaction and the design of intelligent algorithms as interrelated parts of a single design problem. This special issue offers a coherent set of articles on two levels of generality that illustrate the binocular view and help readers to adopt it.

We consider the problem of reconstructing a low rank matrix from noisy observations of a subset of its entries. This task has applications in statistical learning, computer vision, and signal processing. In these contexts, "noise" generically refers to any contribution to the data that is not captured by the low-rank model. In most applications, the noise level is large compared to the underlying signal and it is important to avoid overfitting. In order to tackle this problem, we define a regularized cost function well suited for spectral reconstruction methods. Within a random noise model, and in the large system limit, we prove that the resulting accuracy undergoes a phase transition depending on the noise level and on the fraction of observed entries. The cost function can be minimized using OPTSPACE (a manifold gradient descent algorithm). Numerical simulations show that this approach is competitive with state-of-the-art alternatives.

A fundamental objective in reinforcement learning is the maintenance of a proper balance between exploration and exploitation. This problem becomes more challenging when the agent can only partially observe the states of its environment. In this paper we propose a dual-policy method for jointly learning the agent behavior and the balance between exploration exploitation, in partially observable environments. The method subsumes traditional exploration, in which the agent takes actions to gather information about the environment, and active learning, in which the agent queries an oracle for optimal actions (with an associated cost for employing the oracle). The form of the employed exploration is dictated by the specific problem. Theoretical guarantees are provided concerning the optimality of the balancing of exploration and exploitation. The effectiveness of the method is demonstrated by experimental results on benchmark problems.