Tags: , , , , , , , , , | Categories: Event Posted by bsstahl on 12/11/2017 10:25 PM | Comments (0)

The slide deck for my presentation “Building AI Solutions with Google OR-Tools”, as delivered at SoCalCodeCamp Los Angeles 2017, is available below.

As a reminder, a video of the same session delivered at NDC Sydney in August of 2017 is available on YouTube.

I previously wrote about a Hybrid AI system that combined logical and optimization methods of problem solving to identify the best solution to an employee shift assignment problem. This implementation was notable in that a hybrid approach was used so that the optimal solution could be found, but the system could still indicate to the users why a particular assignment was, or wasn’t, included in the results. 

I recently published to GitHub a demo of a similar system. I use this demo in my presentation, Building AI Solutions that can Reason Why. The code demonstrates the hybridization of multiple AI techniques by creating a solution that iteratively applies a combinatorial optimization engine. Different results are obtained by varying the methods of applying the constraints in that model. In the final (4th) demo  method, an iterative process is used to identify what the shortcomings of the final product are, and why they are necessary.

These demos use the Conference Scheduler AI project to build a valid schedule.

There are 4 examples, each of which reside in a separate test method:

ScheduleWithNoRestrictions()

The 1st method in BasicExamplesDemo.cs shows an unconstrained model where only the hardest of constraints are excluded. That is, the only features of the schedule that are considered by the scheduler are those that are absolute must-haves.  Since there are fewer hard constraints, it is relatively easy to satisfy all the requirements of this model.

ScheduleWithHardConstraints()

The 2nd method in BasicExamplesDemo.cs shows a fully constrained model where  all constraints are considered must-haves. That is, the only schedules that will be considered for our conference are those that meet all of the scheduling criteria. As you might imagine, this can be difficult to do, in this case resulting in No Feasible Solution being found. Because we use a combinatorial optimization model, the system gives us no clues as to  which of the constraints cause the infeasibility, or what to do that might allow it to find a solution.

ScheduleWithTimePreferencesAsAnOptimization()

The 3rd method in BasicExamplesDemo.cs shows the solution when the true must-haves are considered hard constraints but preferences are not. The AI attempts to optimize the solution by satisfying as many of the soft constraints (preferences) as possible. This results in an imperfect, but possibly best case schedule, but one where we have little insight as to what preferences were not satisfied, and almost no insight as to why.

AddConstraintsDemo()

The final demo, and the only method in AddConstraintsDemo.cs, builds on the 3rd demo, where the true must-haves are considered hard constraints but preferences are not. Here however, instead of attempting to optimize the soft constraints, the AI iteratively adds the preferences as hard constraints, one at a time, re-executing the solution after each to make sure the problem has not become infeasible. If the solution has become infeasible, that fact is recorded along with what was being attempted. Then that constraint is removed and the process continues with the remaining constraints. This Hybrid process still results in an imperfect, but best-case schedule. This time however, we not only know what preferences could not be satisfied, we have a good idea as to why.

The Hybrid Process

The process of iteratively executing the optimization, adding constraints one at a time, is show in the diagram below.  It is important to remember that the order in which these constraints are added here is critical since constraining the solution in one way may limit the feasibility of the solution for future constraints.  Great care must be taken in selecting the order that constraints are added in order to obtain the best possible solution.

 

Hybrid Conference Optimization Process

The steps are as follows:

  1. Make sure we can solve the problem without any of the soft constraints.  If the problem doesn’t have any feasible solutions at the start of the process, we are certainly not going to find any by adding constraints.
  2. Add a constraint to the solution. Do so by selecting the next most important constraint in order.  In the case of our conference schedule, we are adding in speaker preferences for when they speak. These preferences are being added in the order that they were requested (first-come first-served).
  3. Verify that there is still at least 1 feasible solution to the problem after the constraint is added.  If no feasible solutions can be found:
    1. Remove the constraint.
    2. Record the details of the constraint.
    3. Record the current state of the model.
  4. Repeat steps 2 & 3 until all constraints have been tried.
  5. Publish the solution
    1. The resulting schedule
    2. The constraints that could not be added.  This tells us what preferences could not be accommodated.
    3. The state of the model at the time the failed constraints were tried.  This give us insight as to why the constraints could not be satisfied.

Note: The sample data in these demos is very loosely based on SoCalCodeCamp San Diego from the summer of 2017. While some of the presenters names and presentations come roughly from the publicly available schedule, pretty much everything else has been fictionalized to make for a compelling demo, including the appearances by some Microsoft rock stars, and the "requests" of the various presenters.

If you have any questions about this code, or about how Hybrid AIs can be used to provide more information about the solutions to problems than strictly optimization or probabilistic models, please contact me on Twitter.

Tags: , , , , , , , , , | Categories: Development Posted by bsstahl on 9/28/2017 1:53 PM | Comments (0)

 

My presentation from the #NDCSydney conference has been published on YouTube.

We depend on Artificial Intelligences to solve many types of problems for us. Some of these problems have more than one possible solution. Handling those problems with more than one solution while building a modern AI system is something every developer will be asked to do over the course of his or her career. Figuring out the best way to utilize the capacity of a device or machine, finding the shortest path between two points, or determining the best way to schedule people or events are all problems where mathematical optimization techniques and tooling can be used to quickly and efficiently find solutions.

This session is a software developers introduction to using mathematical optimization in Artificial Intelligence. In it, we will explore some of the foundational techniques for solving these types of problems, and use the open-source Google OR-Tools to put them to work in our AI systems. Since this is a session for developers, we'll keep it in terms that work best for us. That is, we'll go heavy on the code and lighter on the math.

Tags: , , , , , , , , , | Categories: Event Posted by bsstahl on 6/22/2017 4:10 PM | Comments (0)

The slide deck for my talk “A Developer’s Survey of AI Techniques” can be found below, while the demo code can be found on GitHub.

 

 

The talk explores some of the different techniques used to create Artificial Intelligences using the example of a Chutes & Ladders game.  Various AIs are developed using different strategies for playing a variant of the game, using different techniques for deciding where on the game board to move.

If you would like me to deliver this talk, or any of my talks, at your User Group or Conference, please contact me.

Tags: , , , , , , , , , | Categories: Development Posted by bsstahl on 5/24/2017 8:21 AM | Comments (0)

I've written and spoken before about the importance of using the Strategy Pattern to create maintainable and testable systems. Strategies are even more important, almost to the level of necessity, when building AI systems.

The Strategy Pattern is an abstraction tool used to maintain loose-coupling between an application and the algorithm(s) that it uses to do its job. Since the algorithms used in AI systems have many different ways they could be implemented, it is important to abstract the implementation from the system that uses it. I tend to work with systems that use combinatorial optimization methods to solve their problems, but there are many ways for AIs to make decisions. Machine Learning is one of the hottest methods right now but AI systems can also depend on tried-and-true object-oriented logic. The ability to swap algorithms without changing the underlying system allows us the flexibility to try multiple methods before settling on a specific implementation, or even to switch-out implementations as scenarios or situations change.

When I give conference talks on building AI Systems using optimization methods, I always encourage the attendees to create a "naïve" solution first, before spending a lot of effort to build complicated logic. This allows the developer to understand the problem better than he or she did before doing any implementation. Creating this initial solution has another advantage though, it allows us to define the Strategy interface, giving us a better picture of what our application truly needs. Then, when we set-out to build a production-worthy engine, we do so with the knowledge of exactly what we need to produce.

There is also another component of many AIs that can benefit from the use of the Strategy pattern, and that is the determination of user intent. Many implementations of AI will include a user interaction, perhaps through a text-based interface as in a chatbot or a voice interface such as a personal assistant. Each cloud provider has their own set of services designed to determine the intent of the user based on the text or voice input. Each of these implementations has its own strengths and weaknesses. It is beneficial to be able to swap those mechanisms out at will, along with the ability to implement a "naïve" user intent solution during development, and the ability to mock user intent for testing. The strategy pattern is the right tool for this job as well.

As more and more of our applications depend heavily on algorithms, we will need to make a concerted effort to abstract those algorithms away from our applications to maintain loose-coupling and all of the benefits that loose-coupling provides. This is why I consider the Strategy Pattern to be a necessity when developing Artificial Intelligence solutions.

Tags: , , , , | Categories: Development Posted by bsstahl on 12/15/2016 2:29 PM | Comments (0)

One of my favorite authors among Software Architects, IBM Fellow Grady Booch, made this reference to AlphaGo, IBM’s program built to play the board game Go, in April of 2016:

"...there are things neural networks can't easily do and likely never will. AlphaGo can't reason about why it made a particular move." – Grady Booch

Grady went on to refer to the concept of “Hybrid A.I.” as a means of developing systems that can make complex decisions requiring the processing of huge datasets, while still being able to explain the rationale behind those decisions.

While not exactly the type of system Grady was describing, it reminded me of a solution I was involved with creating that ultimately became a hybrid of an iterative, imperative system and a combinatorial optimization engine.  The resulting solution was able to both determine the optimum solution for a problem with significant data requirements, while still being able to provide information to support the decision, both to prove it was correct, and to help the users learn how to best use it.

The problem looked something like this:

Ideal Solution Space

There are many possible ways to allocate work assignments among employees.  Some of those allocations would not be legal, perhaps because the employee is not qualified for that assignment, or because of time limits on how much he or she can work.  Other options may be legal, but are not ideal.  The assignment may be sub-optimal for the employee who may have a schedule conflict or other preference against that particular assignment, or for the company which may not be able to easily fill the assignment with anyone else.

The complexity in this problem comes from the fact that this diagram is different for each employee to be assigned.  Each employee has their own set of preferences and legalities, and the preferences of the company are probably different for each employee.  It is likely that many employees will not be able to get an assignment that falls into the “Ideal Solution” area of the drawing.  If there were just a few employees and a supervisor was making these decisions, that person would have to explain his or her rationale to the employees who did not get the assignments they wanted, or to the bosses if company requirements could not be met. If an optimization solution made the decisions purely on the basis of a mathematical model, we could be guaranteed the best solution based on our criteria, but would have no way to explain how one person got an assignment that another wanted, or why company preferences were ignored in any individual case.

The resulting hybrid approach started by eliminating illegal options, and then looking at the most important detail and assigning the best fit for that detail to the solution set.  That is, if the most important feature to the model was the wishes of the most senior employee, that employee’s request would be added to the solution. The optimization engine would then be run to be sure that a feasible solution was still available.  As long as an answer could still be found that didn’t violate any of the hard constraints, the selection was fixed in the solution and the next employee’s wishes addressed.  If a feasible solution could not be found using the selected option, that selection would be recorded along with the result of the optimization and the state of the model at the time of processing.  This allows the reasoning behind each decision to be exposed to the users.

A very simplified diagram of the process is shown below.

Hybrid Decision Making

Each time the green diamond testing “Is the solution still feasible?” is hit, the optimization model is run to verify that a solution can be found.  It is this hybrid process, the iterative execution of a combinatorial solution engine, that gives this tool its ability to both answer the question of how to do things, while also being able to answer the question of why it needs to be done this way.

Like Grady, I expect we will see many more examples of these types of hybrids in the very near future.