A number of initiatives are underway for digitalizing real estate transaction processes. Public and private sector bodies are working to automate information retrieval and processing of the financial, ordinance  and  fiscal aspects of such transactions. Other initiatives, such as ours,  are targeted toward helping  stakeholders directly involved in selling and buying  real estate. We present the results from a set of  group sessions, where the focus was on improving the presentation of salient information to sellers and buyers of property. Based on an earlier conceptualization of perceived  information difficulties, we   elicited user stories  for facilitating a better generation, provision and consumption of relevant information for the  residential real estate transaction process. A total of ten services were aggregated from the user stories. We then asked a set of stakeholders to rate the effect of the services on functional objectives; i.e., on how they will affect the transaction process.  We asked stakeholders at the managerial level to rate the functional objectives on strategic objectives. Combining the two sets of ratings, one obtains a rating of perceived benefit for the services, which can help in prioritzing which services to start developing first. In the outset, real estate transactions involve stakeholders with opposing interests. We conclude that multi-stakeholder group sessions can help generate services that serve these conflicting interests on a common ground. 

We develop a logic-based approach for designing simulation-based training scenarios. Our methodology embodies a concise definition of the scenario concept and integrates the notions of training goals, acceptable versus unacceptable actions and performance scoring. The approach applies classical artificial intelligence (AI) planning to extract coherent plays from a causal description of the training domain. The domain- and task-specific parts are defined in a high-level action description language AL. Generic causal and temporal logic is added when the causal theory is compiled into the underlying Answer Set Programming (ASP) language. The ASP representation is used to derive a scoring function that reflects the quality of a play or training session, based on a distinction of states and actions into green (acceptable) and red (unacceptable) ones. To that end, we add to the casual theory a set of norms that specify an initial assignment of colors. The ASP engine uses these norms as axioms and propagates colors by consulting the causal theory. We prove that any set of such norms constitutes a conservative extension of the underlying causal theory. With this work, we hope to lay the foundation for the development of design and analysis tools for exercise managers. We envision a software system that lets an exercise manager view all plays of a tentative scenario design, with expediency information and scores for each possible play. Our approach is applicable to any domain in which means-ends reasoning is pertinent. We illustrate the approach in the domain of crisis response and management.