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I decided that I was going to build in the must and should priorities for my first iteration, and worked up a more detailed picture of what I was going to create. I knew that I wanted to keep track of my work on a day-by-day basis. That more or less decided part of the design: I would need a day-by-day view of my work in the program. I thought about how the Consultants in my business do this at the moment: a paper diary, one page per day. In this diary each Consultant writes out what they did on that day, where they were, and what the charge would be. That system works pretty well, apart from the shortcomings of any paper-based system. What if I modeled the new program on that I was ready to sketch out how the program might look, and came up with the rough design in Figure 9 1.

that occurs in C1 but not in C must be the literal removed by the resolution rule, and therefore its negation must occur in C2 In our example, this indicates that C2 must contain the literal -DHence, C:! = A v -DThe reader can easily verify that applying the resolution rule to C1 and C2 does, in fact, produce the desired resolvent C Notice there is a second possible solution for C2 in the above example In particular, C2 can also be the more specific clause A v -D v B The difference between this and our first solution is that we have now included in C2 a literal that occurred in C1 The general point here is that inverse resolution is not deterministic-in general there may be multiple clauses C2 such that C1 and C2 produce the resolvent C One heuristic for choosing among the alternatives is to prefer shorter clauses over longer clauses, or equivalently, to assume C2 shares no literals in common with C1 If we incorporate this bias toward short clauses, the general statement of this inverse resolution procedure is as shown in Table 106 We can develop rule-learning algorithms based on inverse entailment operators such as inverse resolution In particular, the learning algorithm can use inverse entailment to construct hypotheses that, together with the background information, entail the training data One strategy is to use a sequential covering algorithm to iteratively learn a set of Horn clauses in this way On each iteration, the algorithm selects a training example ( x i , f ( x i ) ) that is not yet covered by previously learned clauses The inverse resolution rule is then applied to

Coursework 1989 B Arch CONCEPTS D SPACE RAPPORTS PERSONNE-SOCIETE-ENVIRONNEMENT LE DESIGN DEPUIS 1960 INTEGRATION DE L ARCHITECTURE AU MILIEU METHODES DE RESTAURATION LABORATOIRE D ARCHITECTURE LE LABORATOIRE D ACOUTIQUE LE LABORATOIRE DE CLIMATOLOGIE ET D ENERGIE LE LABORATOIRE D INFORMATIQUE Qu bec City, Qu bec

1 Given initial clauses C1 and C, find a literal L that occurs in clause C1, but not in clause C 2 Form the second clause Cz by including the following literals

It still looks like the diary page, but I have started to separate out what data I expect to provide for this journal entry, what I would like to be able to look up, and what should be calculated. This was starting to suggest a data model. Before creating the data model in detail I needed to think about the entities I would be managing in general. I needed to keep track of clients, and each client might have several projects, and there could be many journal entries for any project. Also, since there are a number of Consultants who work in the company, I needed to store information about them too. This was starting to suggest a complexity further down the road is this a multiuser system, with login and user-management features No . . . this was a good time to put my Agile foot down: this is not a high-priority feature, and, while keeping in mind the possibility (and therefore not consciously designing a system that cannot be multiuser), I decided not to do anything more about it yet, beyond allowing the creation and management of multiple Consultants.

TABLE 106 Inverse resolution operator (propositional form) Given two clauses C and Cl this computes a clause C2 such that C A Cz I- C 1