Main topics:
How good of an explanation can be provided by Artificial Intelligence built using statistical learning methods? This note is slightly more complicated than my usual ones. In logic, conclusions are computed from premises by applying well defined rules. When a conclusion is the appropriate one, given the premises and the rules, then it is said…
If a requirement can be easily rephrased as a criterion in a decision problem (as I argued elsewhere), then what is advice?
What can you do to influence someone’s decision, if you cannot give them advice? In short, a possible approach is to take actions that satisfy two conditions: I call this the nurturing of choice. Instead of providing advice that is clearly directed at the elements of the decision problem (as I discussed in my book…
This text goes into the details of a single Define/Destroy iteration, in a project I was part of in 2017. I show how the Define/Destroy iteration was done, including the detail of the glossary built in the Define part of the iteration, and the glossary remade as a result of the Destroy part of that first iteration.
“Objective”, as in, for example, “what I’m saying is objective”, or “that statement is objective”, or “we need objective criteria when making these decisions”, is a complicated term. It takes a lot of effort to make sure it is understood as intended (or closely enough). It is therefore a costly word to use. Why is…
There is no single definition of the term “evidence”, and trying to make one isn’t the purpose of this text. But there are ways of telling if something might be evidence, and knowing when it clearly isn’t. Such knowledge helps you develop a taste, so to speak, in evidence. Isn’t that valuable, given how frequently you may be giving evidence to support your ideas, and how frequently others do the same to you?
Many people spent a lot of time, across centuries, trying to build good explanations, and trying to distinguish good from bad ones. While there is no consensus on what “explanation” is (always and everywhere), it is worth knowing what good explanations may have in common. It helps develop a taste in explanations, which is certainly helpful given how frequently you may need to explain something, and how often others offered explanations to you.
Graphical Requirements Modeling (GRM) consists of representing requirements in diagrams: requirements (and other relevant information) are represented as nodes, and relationships between them as edges. Relationships can show, for example, that one requirement refines another, that some are in conflict with others, that they are more or less desirable, and so on. Various software tools…
This paper addresses the modelling of requirements for a content Recommendation System (RS) for Online Social Networks (OSNs). On OSNs, a user switches roles constantly between content generator and content receiver. The goals and softgoals are different when the user is generating a post, as opposed as replying to a post. In other words, the…
In our prior work, we identified rules for use in recommendation algorithms on Online Social Network (OSN) in order to increase the relevance of content suggested to a user. The resulting recommendation algorithms filter out and prioritize event types for OSN users (such as photo posts by friends, status posts, shared content, etc.), and are…
Knowledge-Base Recommendation (or Recommender) Systems (KBRS) provide the user with advice about a decision to make or an action to take. KBRS rely on knowledge provided by human experts, encoded in the system and applied to input data, in order to generate recommendations. This survey overviews the main ideas characterizing a KBRS. Using a classification…
Advice involves recommendations on what to think; through thought, on what to choose; and via choices, on how to act. Advice is information that moves by communication, from advisors to the recipient of advice. This book offers a general way to analyze advice. The analysis applies regardless of what the advice is about and from…