xFS-bots 0.0.2

A suite of offline bots built on top of classical NP-Complete problems. Friendly for cryptographic operations.

xFS

Graph Problems of Interest (in my words)

Here is a list of graph problems in the project:

• Respondent Network Bot (Alpha)
  • vantage point for learning: Q, the questioner.
  • file: graph_problems/rnb.py
• Hidden Threat Exposure Bot (Alpha)
  • vantage point for learning: navigator of network containing threats.
  • file: graph_problems/hte.py
• Slander Net Bot (Alpha)
  • vantage point for learning: agent that determines what communication ports to open and close to maximize objective function score.
  • code has non-deterministic elements to it. Required to set seeds for Python and Numpy random.
  • file: graph_problems/snb.py
• Poison Trace Bot (Alpha)
  • vantage point for learning: agent (poison target), in a network, that has to position alert relays to help it know (poison source, poison identity) of poison delivered to it. Accurate knowledge allows for poison target to negate poison before its termination.
  • code has non-deterministic elements to it. Required to set seeds for Python and Numpy random.
  • file: graph_problems/ptb.py
• Bull Killer Bot (Alpha)
  • vantage point for learning: Chaser agents that pursue the Bull along a network.
  • file: graph_problems/bkb.py
• Homo Frame Bot (Alpha)
  • vantage point for learning: n agents expected to act according to homomorphic demands, set by administrator.
  • file: graph_problems/hfb.py
• Mob Killer Bot (Alpha)
  • vantage point for learning: anti-mob unit acting against n Mob agents in a reactive system.
  • file: graph_problems/mkb.py
• Strangle Bot (Alpha)
  • vantage point for learning: strangling agent with objective to control all nodes of graph G, the subject of strangling.
  • file: graph_problems/sb.py
• Controverter Bot (Alpha)
  • vantage point for learning: an agent in a multi-agent cyclical game, structured as a continually variable chain of decision junction points, each requiring all the agents to make their moves.
  • file: graph_problems/cb.py
• Token-Swapping Bot (Alpha)
  • vantage point for learning: an agent that has to solve the NP-Complete problem, Token Swapping, for an arbitrary graph.
  • file: graph_problems/tsb.py
• Dual Role Bot (Alpha)
  • vantage point for learning: a dual agent that has to complete n demands, each demand consisting of an independent and a third-party component, with the least cost from conflicts of independence within and between the demands.
  • NOTE: problem can be reduced to the NP-Hard problem, Set Cover.
  • file: graph_problems/drb.py
• Middleman Bot (Alpha)
  • vantage point for learning: a buying agent that is to buy x units of a product through a network of sellers, the network starting with one original seller and n middle agents, at the lowest cumulative price.
  • file: graph_problems/mmb.py
• Vector-Tracking Bot (Alpha)
  • vantage point for learning: a group of n > 1 tracking agents that is to track a moving target, over q rounds, with the primary goal of minimal cumulative euclidean distance with the target and the secondary goal of maintaining symmetric balance with one another.
  • file: graph_problems/vtb.py
• Introspection Bot (Alpha)
  • vantage point for learning: obtaining the same cyclical node output and shortest paths as a reference result in a simple undirected graph, traversed through a breadth-first or depth-first process.
  • file: graph_problems/ib.py
• Probabilistic Impact Bot (Alpha)
  • vantage point for learning: defending against each of an offendor's moves by correctly predicting the move; based on the Hidden Markov Model.
  • file: graph_problems/pib.py
• Ends-Fixated Bot (Alpha)
  • vantage point for learning: navigating a directed graph from start node to end node, such that each node is associated with an activation function navigator must pass, via providing appropriate support values (real numbers) to the nodes of relevance.
  • file: graph_problems/efb.py
• Two Faces Three Motives Bot (Alpha)
  • vantage point for learning: an agent, connected to two other agents in a trifecta arrangement, that has to execute its independently selected move at every timestamp.
  • file: graph_problems/tftmb.py
• Proaction-Driven Inadvertency Bot (Alpha)
  • vantage point for learning: executing n three-noded steps with the least number of inadvertencies activated.
  • file: graph_problems/pdib.py

NOTE:

Some of these bots rely on Python/Numpy random. User results may differ from developer results. As of this time in writing, developer Python version is 3.14.2.

NOTE:

The methods provided in file<graph_problems.easy_gen> allow for easy generation of these bots (less instantiating parameters).

The Machine-Learning Aspect

The graph problems defined in this project involve software agents. These agents are programmed with functionalities that are static. This implies the agents cannot "learn" any more, past their programming, about the specific problems they act in. Their learning mechanisms cannot "expand" any more outside of this static programming: no additional variables of interest, no different data structure formats, no different ordering scheme of deciding on the best choice per timestamp, no different ranking mechanisms for choices. Instead, the only way to improve a solution is through a semi-blind search process that iterates through a candidate list of pseudo-random number generators (PRNGs) for the best PRNG. PRNGs are used in agent decision-making for these graph problems. PRNGs output numbers, and agents map these numbers out to decisions taken by them. The utility of obtaining a high-performing PRNG for a graph problem of specific starting parameters is restricted to that case. There is no software mechanism provided to automate deriving of further insight into any graph problem example via a high-performing PRNG. This deficit differs from traditional machine-learning problems on fixed and labeled datasets, where feedback loops of training iterations can be guaranteed to yield incrementally better solutions, until the global optimum is found. This guarantee, of course, is theoretical and cannot be stated for deep learning problems on highly variable datasets. It is possible to implement further code for these graph problems to be more conducive for machine-learning. However, implementing further code would defeat the purpose of graph problem difficulty. The division of partial information between agents in every one of these graph problems was deliberate in fulfillment of maintaining a baseline of probabilistic difficulty for an agent to improve its solution (PRNG). Unlike the highly vectorizable (input,output) samples of traditional machine-learning that typically operates in Euclidean space, finding a high-scoring solution does not easily lead to a better solution derived from that one. Users can refer to one high-scoring PRNG, and add tweaks to it at selected indices of the generator output. See below for illustration on PRNG G and tweaking it to G2:

G:  x0,x1,x2,x3,x4,x5... 
G2: x0,x42,x2,x56,x4,x5,... 

But this is a cumbersome process, and there is no guarantee making tweaks to a high-performing generator would result in a better solution.

User Access to the Graybox

The computer code for these bots in these graph problems is open for view. This is not a blackbox arrangement. Users, in their attempts to guess high-performing PRNGs for any graph problem example, can carefully review how the automata decision-making pipelines work. These pipelines follow a general form, and descriptions are provided for each of these graph problem bots. But these implementations rest on arbitrary design.

In a blackbox system, users would not know what the graph problem is, let alone the general form of it. They would only be able to enter in a PRNG for the automaton to use for decision-making. They would not be able to ascertain the end results from the PRNG. In these graybox implementations, users would know the end results from the PRNG. But since a PRNG only outputs real numbers for decision-making, users would not be able to easily know what decisions the PRNG was responsible for. Thus, users would have a relatively difficult time knowing what decisions are sub-optimal. Users can analyze the code implementation to determine the specific decisions the PRNG mapped to, post-simulation. But code is not provided in this project to convenience that.