Multiagent Partially Observable Markov Decision Processes (MPOMDPs) provide a powerful framework for optimal decision making under the assumption of instantaneous communication. We focus on a delayed communication setting (MPOMDP-DC), in which broadcasted information is delayed by at most one time step. This model allows agents to act on their most recent (private) observation. Such an assumption is a strict generalization over having agents wait until the global information is available and is more appropriate for applications in which response time is critical. In this setting, however, value function backups are significantly more costly, and naive application of incremental pruning, the core of many state-of-the-art optimal POMDP techniques, is intractable. In this paper, we overcome this problem by demonstrating that computation of the MPOMDP-DC backup can be structured as a tree and introducing two novel tree-based pruning techniques that exploit this structure in an effective way. We experimentally show that these methods have the potential to outperform naive incremental pruning by orders of magnitude, allowing for the solution of larger problems.

The better the team mental model, the better the teamwork. An important aspect of what determines a good team model is the extent to which the model is shared by the team members. This paper presents suggestions for measuring the extent to which teams have a shared mental model and describes how these measures are related to team performance. The most promising measures of sharedness proposed so far rely on using a compositional approach for team modeling and on a situation-sensitive relevance relation that indicates to what extent components contribute to team performance. A case study illustrates the approach and initial results on measuring performance when teams use different levels of sharedness.

This paper presents a training game intended to train crisis management teams to negotiate collaboratively in order to reach the group goal in the best way possible. The importance of the group goal in comparison to their individual goals is touched upon as well, as are various conflicts that can occur during such a negotiation. The game, which is implemented in the Blocks World 4 Teams environment, gives a team a specific scenario and allows them to negotiate a plan of action. This plan of action is then performed by agents, after which the team members will be debriefed on their performance. An experiment, containing multiple rounds to test the effect the game has on participants, is planned in the near future.

We present a spectrum-based, sequential software debugging approach coined Sequoia, that greedily selects tests out of a suite of tests to narrow down the set of diagnostic candidates with a minimum number of tests. Sequoia handles multiple faults, that can be intermittent, at polynomial time and space complexity, due to a novel, approximate diagnostic entropy estimation approach, which considers the subset of diagnoses that cover almost all Bayesian posterior probability mass. Synthetic experiments show that Sequoia achieves much better diagnostic uncertainty reduction compared to random test sequencing.Real programs, taken from the Software Infrastructure Repository, confirm Sequoia's better performance, with a test reduction up to 80% compared to random test sequences.

Geotagged photos of users on social media sites provide abundant location-based data, which can be exploited for various location-based services, such as travel recommendation. In this paper, we propose a novel approach to a new application, i.e., personalized landmark recommendation based on users’ geotagged photos. We formulate the landmark recommendation task as a collaborative filtering problem, for which we propose a category-regularized matrix factorization approach that integrates both user-landmark preference and category-based landmark similarity. We collected geotagged photos from Flickr and landmark categories from Wikipedia for our experiments. Our experimental results demonstrate that the proposed approach outperforms popularity-based landmark recommendation and a basic matrix factorization approach in recommending personalized landmarks that are less visited by the population as a whole.

The research project here described aims to contribute to the issue of sustainability of buildings by improving the architectural design process with the development of a decision support tool for the architect. In particular, the research adopts the improvement of existing designs, namely encouraging energy-efficient redesigns while improving indoor environmental quality as its strategy to promote sustainability. Redesign strategy is considered not only to extend the life cycle of a building but also to contribute to the realization of the overall transition towards an efficient and clean climate. The starting point for this research is the question of how to develop an integral framework which enables the modelling of design knowledge through more energy-efficient dwellings with acceptable indoor comfort in the sustainability context so that it would be possible to deal with qualitative, quantitative, complex and contradictory information at the same time and integrate these into design decision-making processes. This modelling approach is considered to provide a link to developing a tool or a link to be embedded in an existing tool. In the development of such an approach, how Artificial Intelligence (AI) can facilitate an integral understanding of the aspects is raised as a methodological question in terms of information processing and knowledge integration in the form of a design decision support tool. By this way it will be possible to assess the performance of the end result with respect to design choices, beforehand.

Procedural content generation is becoming more and more relevant to solve the problem of content creation for the ever growing virtual worlds of games, simulations and other applications. However, these procedures are often unintuitive or use vague parameters, making it somewhat difficult for a designer to express his or her creative intent. Even worse, most of these techniques lack an accessible and easy to use interface.We have developed a generic layout solving approach to automatically create sensible content for virtual worlds. In that context, this paper proposes a high-level scene description language that allows designers to specify particular types of scenes. This description language allows designers to easily specify which objects need to be present in a scene, their attributes, and possible interrelationships. Application of the language, based on the rich vocabulary taken from a semantic library, is illustrated with several examples, showing its flexibility, intuitiveness and ease of use.

Efficient management and propagation of temporal constraints is important for temporal planning as well as for scheduling. During plan development, new events and temporal constraints are added and existing constraints may be tightened; the consistency of the whole temporal network is frequently checked; and results of constraint propagation guide further search. Recent work shows that enforcing partial path consistency provides an efficient means of propagating temporal information for the popular Simple Temporal Network (STN). We show that partial path consistency can be enforced incrementally, thus exploiting the similarities of the constraint network between subsequent edge tightenings. We prove that the worst-case time complexity of our algorithm can be bounded both by the number of edges in the chordal graph (which is better than the previous bound of the number of vertices squared), and by the degree of the chordal graph times the number of vertices incident on updated edges. We show that for many sparse graphs, the latter bound is better than that of the previously best-known approaches. In addition, our algorithm requires space only linear in the number of edges of the chordal graph, whereas earlier work uses space quadratic in the number of vertices. Finally, empirical results show that when incrementally solving sparse STNs, stemming from problems such as Hierarchical Task Network planning, our approach outperforms extant algorithms.