Week 5

TMK Modeling Progress

Earlier this week, I started integrating the means-end analysis concept into both the knowledge, the task and the method model. Additionally, I got the idea to implement a finite state machine (FSM) in the method model from the classification method model example from here: GitHub Link. In the current setup, the task still has a 1:1 mapping with the methods, and the method “Means-End Analysis” now includes the FSM and transitions. But I’m unsure about my use of the “type”: “data condition” and the specific condition needed for the transition, which I’ve referred to as “datacondition” in the FSM. I’ve raised this with Erin and hopefully I can get some guidance on the following.

After the sync-up with the IVY Team on Wednesday, I decided to add another transition in order to do an iteration of the means-end problem solving until delta is zero. The transition “answer selected” directs the flow from “Answer Selection” method back to “Set Up Problem” method if the delta counter is not zero, indicating that the problem hasn’t been fully solved and another set of block arrangements needs to be processed. Conversely, the transition “problem solved” moves from “Answer Selection” to “Success” when the delta counter is zero, signifying that the problem has been solved successfully. These transitions ensure the process iterates until an optimal solution is found.

Since Rahul’s suggestion of incorportating this for Ravens Progressive Matrices (RPM) problem, I can see some parallels of the FSM in the method model that can be adapted from Blocks World (BW) to Raven’s (RPM):

  • Set Up The Problem (BW) -> Set Up The Problem (RPM) - validate and create an instance of the problem setup, define the initial state and goal state for each problem
  • Individual Position Extraction (BW) -> Attribute Extraction (RPM) - extract and identify key transformations between figures in the RPM
  • Position Check (BW) -> Attribute Comparison (RPM) - compare the extracted attributes of each figure to identify patterns and transformations
  • Delta Counter (BW) -> Delta Counter (RPM) - quantify the differences (delta) between the current figure and the goal figure using transformation distance
  • Answer Selection (BW) -> Pattern Selection (RPM) - select the correct answer based on the lowest delta, identifying the figure that best completes the matrix

For RPM I can see the need for individual operators for the transformation and transformation distance calculator. Individual Operators: to define and apply operators (like rotation, scaling) iteratively to move closer to the goal figure Transformation Distance Calculator Operator: to get a numerical value of the current distance – still unsure of how to engineer this, but maybe modeling this might be okay.

DI Lab Meeting and Knowledge Share

The team reviewed my labmate’s presentation to the NSF and provided updates. There was discussion around standardizing TMK models, developing a modular architecture, addressing challenges like hierarchical decomposition, and assigning more complex skills to build understanding. My action item is to peer review the block world TMK models that have been developed to ensure clarity and consistency with standards.

The knowledge sharing session revolved around the use of generative AI in planning and supply chain management, with the presenter highlighting the limitations and opportunities in autonomous planning. Audrey (presenter) also discussed the challenges of using language models (LLMs) for planning tasks, while others joined in and explored various approaches to integrating generative AI with knowledge representations. The conversation also touched on the basis of planning in logic and the potential applications of LLMs in education, naturally since the DI Lab focuses on EduTech. Surprisingly, Generative AI like GPT-4 performed poorly as an autonomous planner, with an average success rate of only 12% across domains. Fine-tuning and using other models did not significantly improve performance. I thought it was interesting that generative AI still struggled in situations where the input was abnormal or unexpected, like when block or action names were obfuscated in the “Mystery Blocks World” experiments. At the end, we discussed the potential and limitations of AI in decision-making and planning, and the need for further research in this area.

Literature Review on Task-Method-Knowledge (TMK) and Hierarchical Transfer Network (HTN)

Similarly, as suggested by Dr. Ashok, I read the paper “A Study of Process Languages for Planning Tasks” by Stephen Lee-Urban, Héctor Muñoz-Avila. It briefly discusses the definition of a process and the importance of reuse and modification of processes to achieve knowledge transfer. TMK is both a process language and a process model. Its downfall, which is the lack of clear semantics, can be remedied by conversion to HTNs. HTN stands for Hierarchical Task Network and uses high-level tasks decomposed into simpler ones until reduced to primitive actions. HTN planning is encompassed in the Methods of TMK. The expressivity of two languages is demonstrated when there is a polynomial or Turing computable transformation between them. The translation from TMK to HTN starts with the translation process, which converts TMKs to HTNs by processing each TMK method recursively, translating statements, and constructing HTN methods from the results. To handle nested statements, recursive calls are implemented, breaking them down from the most to the least deeply nested. This method ensures a clear equivalence between TMKs and HTNs, but more compact translation can be achieved with PDDL (a STRIPS-based planning language). The toHTNs procedure translates TMK statements into HTN methods. It starts with a sequence of TMK statements and processes them in reverse order. For a single statement with no body, a new task is created and appended as the last subtask. If the statement has a body, a recursive call is made to process it. For multiple statements, each is translated in reverse order with recursive calls, updating the task for each iteration. The result is a collection of HTN methods and the final task.

Other Reading

I continued reading Animal Farm by George Orwell and last week I stopped at the point where the animal manor got their independence from humans, established their own society, and had been initially enjoying the things that have changed since the revolution. This week, I got to a point where things started to get dark. Two of the original leaders, who were debating in a civilized manner, were no more. Napoleon (leader 1) manipulated the animals into accepting the violent expulsion of Snowball (leader 2). It seemed to me that the story is akin to a frog in slowly increasing hot water to boil. I get some chills seeing that this happens to the world time and time again.

Personally, this week I have traveled from Porto, Portugal, to Santiago de Compostela in northern Spain, specifically in the autonomous region of Galicia. This means some hours have been spent in a long bus ride and thus, some time to read, ponder on the things I have learned while traveling, and anticipate a language and culture change at the new city.

I began the analytical reading section in How to Read a Book: The Classic Guide to Intelligent Reading. Again, it was emphasized that classifying a book is akin to the author waving flags and clues to tell the reader where the wind blows, or what kind of knowledge can be had with it. An interesting point was the spotlight on why readers eschew paying attention to titles and reading prefaces; it is because classifying a book is not attained nor contemplating the question “What is this book about?” is not done. Another thing was how expository books can be classified as either theoretical and practical – mainly theoretical answers what is the case and practical shows how it is done. A person could deem a practical book “unpractical” because he thinks it doesn’t work, but that shouldn’t be the case, because the classification term doesn’t concern if one thinks it works or not.