JARET: A Human Assistive A.I. Agent for Goal Review and Time Management

Many students do not set goals or plan their time weekly (due to lack of ability, perceived difficulty, and other reasons) resulting in procrastination, stress, and lower academic performance. This paper presents the design methodology and considerations for a human assistive AI agent that helps students review and plan for study goals, reducing a large abstract problem into a set of simpler review tasks. J.A.R.E.T. (Just A Recommender Engine for Time) uses key principles from Self-Regulated Learning and Cognitive Load Theory in an interactive system that guides students through focused goal review and planning tasks, then uses a constraint satisfaction AI agent to assemble a proposed calendar schedule designed to help achieve the student’s goals. The AI agent uses hard and soft constraints with a value function designed and searches for a best fit that follows constraints while trying to also fit student preferences. Results show that the design is able to reliably build recommended solutions when constraints and preferences are reasonable and not overly restrictive.

An Intelligent Technique for Solving Timetable Problem

Timetabling problem is complex combinatorial resources allocation problems. There are two hard and soft constraints to be satisfied. The timetable is feasible if all hard constraints are satisfied. Besides, satisfying more of the soft constraints produces a high-quality timetable. Crow Search Algorithm (CSA) as an intelligence technique presents for solving timetable problem. CSA like all meta-heuristic optimization techniques is a nature-inspire of intelligent behavior of crows. The proposed CSA tested using the well-known benchmark of hard timetabling datasets (hdtt). Taguchi’s method used to tune the best parameter combinations for the factors and levels. The tuned parameters of CSA are applied on datasets in separate experiment. The results show that the proposed CSA is superior to generate solutions in reasonable CPU time when compared with other literature techniques.

Chapter 23. MaxSAT, Hard and Soft Constraints

MaxSAT solving is becoming a competitive generic approach for solving combinatorial optimization problems, partly due to the development of new solving techniques that have been recently incorporated into modern MaxSAT solvers, and to the challenge problems posed at the MaxSAT Evaluations. In this chapter we present the most relevant results on both approximate and exact MaxSAT solving, and survey in more detail the techniques that have proven to be useful in branch and bound MaxSAT and Weighted MaxSAT solvers. Among such techniques, we pay special attention to the definition of good quality lower bounds, powerful inference rules, clever variable selection heuristics and suitable data structures. Moreover, we discuss the advantages of dealing with hard and soft constraints in the Partial MaxSAT formalims, and present a summary of the MaxSAT Evaluations that have been organized so far as affiliated events of the International Conference on Theory and Applications of Satisfiability Testing.

Timetable Generation

Evidence based on ongoing published research shows that timetabling has been a challenge for over two decades. There is a growing need in higher education for a learner-centered solution focused on individual preferences. In the authors' earlier published work, students' group assessment information was mined to determine individualized achievements and predict future performance. In this paper, they extend the work to present a solution that uses students' individualized achievements, expected future performance, and historical registration records to discover students' registration timing patterns, as well as the most appropriate courses for registration. Such information is then processed to build the most suitable timetable for each student in the following semester. Faculty members' time preferences are also predicted based on historical teaching time patterns and course teaching preferences. The authors propose a modified frequent pattern (FP)-tree algorithm to process the predicted information. This results in clustering students to solve the timetable problem based on the predicted courses for registration. Then, it divides the timetable problem into subproblems for resolution. This ensures that time will not conflict within the generated timetables while satisfying both the hard and soft constraints. Both students' and faculty members timetabling preferences are met (88.8% and 85%).

Genetic Algorithm for Exam Timetabling Problem - A Specific Case for Japanese University Final Presentation Timetabling

This paper presents a Genetic Algorithm approach to solve a specific examination timetabling problem which is common in Japanese Universities. The model is programmed in Excel VBA programming language, which can be run on the Microsoft Office Excel worksheets directly. The model uses direct chromosome representation. To satisfy hard and soft constraints, constraint-based initialization operation, constraint-based crossover operation and penalty points system are implemented. To further improve the result quality of the algorithm, this paper designed an improvement called initial population pre-training. The proposed model was tested by the real data from Sophia University, Tokyo, Japan. The model shows acceptable results, and the comparison of results proves that the initial population pre-training approach can improve the result quality.

Courses timetabling based on hill climbing algorithm

In addition to its monotonous nature and excessive time requirements, the manual school timetable scheduling often leads to more than one class being assigned to the same instructor, or more than one instructor being assigned to the same classroom during the same slot time, or even leads to exercise in intentional partialities in favor of a particular group of instructors. In this paper, an automated school timetable scheduling is presented to help overcome the traditional conflicts inherent in the manual scheduling approach. In this approach, hill climbing algorithms have been modified to transact hard and soft constraints. Soft constraints are not easy to be satisfied typically, but hard constraints are obligated. The implementation of this technique has been successfully experimented in different schools with various kinds of side constraints. Results show that the initial solution can be improved by 72% towards the optimal solution within the first 5 seconds and by 50% from the second iteration while the optimal solution will be achieved after 15 iterations ensuring that more than 50% of scientific courses will take place in the early slots time while more than 50% of non-scientific courses will take place during the later time's slots.

The ambiguity of nestedness under soft and hard constraints

Many real networks feature the property of nestedness, i.e. the neighbours of nodes with a few connections are hierarchically nested within the neighbours of nodes with more connections. Despite the abstract simplicity of this notion, various mathematical definitions of nestedness have been proposed, sometimes giving contrasting results. Moreover, there is an ongoing debate on the statistical significance of nestedness, since random networks where the number of connections (degree) of each node is fixed to its empirical value are typically as nested as real ones. By using only ergodic and unbiased null models, we propose a clarification that exploits the recent finding that random networks where the degrees are enforced as hard constraints (microcanonical ensembles) are thermodynamically different from random networks where the degrees are enforced as soft constraints (canonical ensembles). Indeed, alternative definitions of nestedness can be negatively correlated in the microcanonical one, while being positively correlated in the canonical one. This result disentangles distinct notions of nestedness captured by different metrics and highlights the importance of making a principled choice between hard and soft constraints in null models of ecological networks.