This paper discusses the systematic use of product feedback information to support life-cycle design approaches and provides guidelines for developing a design at both the product and the system levels. Design activities are surveyed in the light of the product life cycle, and the design information flow is interpreted from a semiotic perspective. The natural evolution of a design is considered, the notion of design expectations is introduced, and the importance of evaluation of these expectations in dynamic environments is argued. Possible strategies for reconciliation of the expectations and environmental factors are described. An Internet-enabled technology is proposed to monitor product functionality, usage, and operational environment and supply the designer with relevant information. A pilot study of assessing design expectations of a refrigerator is outlined, and conclusions are drawn.

If a smooth, closed, and embedded curve is deformed along its normal vector field at a rate proportional to its curvature, it shrinks to a circular poin t. This curve evolution is called Euclidean curve shortening and the result is known as the Gage-Hamilton-Gra yson Theorem. Motivated by the rendezvous problem for mobile autonomous robots, we address the proble m of creating a polygon shortening flow. A linear scheme is proposed that exhibits several analogues to Euclidean curve shortening: The polygon shrinks to an elliptical point, convex polygons remain conv ex, and the perimeter of the polygon is monotonically decreasing. This paper studies the rendezvous problem for mobile autonomous robots, in which the goal is to develop a local control strategy that will drive each robots's state (usually its position) to a common value. Research on this problem has been performed in discre te and continuous time.

This paper is based on and advocates the trend in software engineering of extending the use of software patterns as means of structuring solutions to software development problems (be they motivated by best practice or by company interests and policies). The paper argues that, on the one hand, this development requires tools for automatic organisation, retrieval and explanation of software patterns. On the other hand, that the existence of such tools itself will facilitate the further development and employment of patterns in the software development process. The paper analyses existing pattern representations and concludes that they are inadequate for the kind of automation intended here. Adopting a standpoint similar to that taken in the semantic web, the paper proposes that feasible solutions can be built on the basis of ontological representations.

Consider an agent interacting with an environment in cycles. In every interaction cycle the agent is rewarded for its performance. We compare the average reward U from cycle 1 to m (average value) with the future discounted reward V from cycle k to infinity (discounted value). We consider essentially arbitrary (non-geometric) discount sequences and arbitrary reward sequences (non-MDP environments). We show that asymptotically U for m->infinity and V for k->infinity are equal, provided both limits exist. Further, if the effective horizon grows linearly with k or faster, then existence of the limit of U implies that the limit of V exists. Conversely, if the effective horizon grows linearly with k or slower, then existence of the limit of V implies that the limit of U exists.

The paper presents two equivalent definitions of answer sets for logic programs with aggregates. These definitions build on the notion of unfolding of aggregates, and they are aimed at creating methodologies to translate logic programs with aggregates to normal logic programs or positive programs, whose answer set semantics can be used to defined the semantics of the original programs. The first definition provides an alternative view of the semantics for logic programming with aggregates described by Pelov et al. The second definition is similar to the traditional answer set definition for normal logic programs, in that, given a logic program with aggregates and an interpretation, the unfolding process produces a positive program. The paper shows how this definition can be extended to consider aggregates in the head of the rules. The proposed views of logic programming with aggregates are simple and coincide with the ultimate stable model semantics, and with other semantic characterizations for large classes of program (e.g., programs with monotone aggregates and programs that are aggregate-stratified). Moreover, it can be directly employed to support an implementation using available answer set solvers. The paper describes a system, called ASP^A, that is capable of computing answer sets of programs with arbitrary (e.g., recursively defined) aggregates.

Clickthrough data is a particularly inexpensive and plentiful resource to obtain implicit relevance feedback for improving and personalizing search engines. However, it is well known that the probability of a user clicking on a result is strongly biased toward documents presented higher in the result set irrespective of relevance. We introduce a simple method to modify the presentation of search results that provably gives relevance judgments that are unaffected by presentation bias under reasonable assumptions. We validate this property of the training data in interactive real world experiments. Finally, we show that using these unbiased relevance judgments learning methods can be guaranteed to converge to an ideal ranking given sufficient data.

This paper evaluates the robustness of learning from implicit feedback in web search. In particular, we create a model of user behavior by drawing upon user studies in laboratory and real-world settings. The model is used to understand the effect of user behavior on the performance of a learning algorithm for ranked retrieval. We explore a wide range of possible user behaviors and find that learning from implicit feedback can be surprisingly robust. This complements previous results that demonstrated our algorithm's effectiveness in a real-world search engine application.

This paper presents a novel approach for using clickthrough data to learn ranked retrieval functions for web search results. We observe that users searching the web often perform a sequence, or chain, of queries with a similar information need. Using query chains, we generate new types of preference judgments from search engine logs, thus taking advantage of user intelligence in reformulating queries. To validate our method we perform a controlled user study comparing generated preference judgments to explicit relevance judgments. We also implemented a real-world search engine to test our approach, using a modified ranking SVM to learn an improved ranking function from preference data. Our results demonstrate significant improvements in the ranking given by the search engine. The learned rankings outperform both a static ranking function, as well as one trained without considering query chains.

E-learning is nowadays one of the most interesting of the "e- " domains available through the Internet. The main problem to create a Web-based, virtual environment is to model the traditional domain and to implement the model using the most suitable technologies. We analyzed the distance learning domain and investigated the possibility to implement some e-learning services using mobile agent technologies. This paper presents a model of the Student Assessment Service (SAS) and an agent-based framework developed to be used for implementing specific applications. A specific Student Assessment application that relies on the framework was developed.

The se s tructur es were embod ie d into a comprehensive agent be havi oral model shaped on t op of a unifying framework. By means of s uch a fr amework we managed to make the agent p la tform trans pare nt to the us er and, in the same time, deco uple the re us able patterns from the under lying mobile agent pl atfo rm. It thus beco mes cl ear that the model was s tructur ed to be highly indepe nd ent, encompas sing a handful of abst ract featur es that a llo w it to be eq ually expres sive re gardle s s of th e underlying agent suppor t . Enti ties common to eve ry agent p la tfor m (location, agent, mes s age, behavior, agent identifier along with ot her relevant ones) provi d e the cont ext within which we were able to d efine the reus ab le patterns . The s e patterns prod uc e an environment that ultimately sep arate s the behavi oral model from the a ctual s keleton upon which the pat ters are enacted (i.e. the J ADE agent plat fo rm) and, as s uch, once they are c re ated, rewriting them will not be necessary for every new p la tfor m. Simply put, one has onl y to write new ada p te rs if needed, or us e the avail able ones a long with the alread y exi s ting framewo rk items to integrate (coale sce) the compon ent sh e req ui res. Adapters were employed t o p rovi de the bridge between the framework and agent p la tfor ms .