AIMech AI

Ant-based telephone routing

Algorithms AIMech:AntBasedTelephoneRouting DRAFT
A distributed telephone-routing algorithm that adapts neighbor-choice probabilities using mobile ant agents. Ants sample moves from destination-specific rows but reinforce entries toward their own source, with age and congestion delay modulating the updates. New calls follow the largest entries of the resulting destination rows in the source’s simulated circuit-switched network.
Identity Notes & Concept Scope This identity selects the HPL-96-76 telephone controller with source-row reinforcement and separate stochastic-ant and largest-entry call routing policies. ABC remains source terminology, not an alias that would identify artificial bee colony. AntNet, AntColonyOptimization, QRouting and the learning-automata family retain distinct identities and mechanisms. Fresh postmerge collection on 15 October 2026 at base a80a4e53097977b397421562fb1169aa1426dc8b inspected 757 maintained and 442 pinned AIO identities, including 4,789 identity-field occurrences and 3,661 normalized values. Four fixed-string, word-boundary searches included ignored and hidden files in the original checkout and three discovery/recovery checkouts: 8,131, 7,634, 18,826 and 22,178 occurrences, grouped into 3,163 exact contents. Candidate identities had no collision; the four PSO self-matches were explicitly retained for enrichment. Root read 159 decoded candidate contexts (including a separately retained two-context key/value projection correction), all 30 normalized match fields and relevant frontier fields/selected full owner objects. Supporting agents assessed the disjoint candidate and two contextual partitions; full raw-content reconstruction is separate from semantic assessment and is not a claim that root read all raw containers. Existing unrelated definitions, rendered mirrors, historical report/review contexts and incidental substrings do not establish equivalence. The frontier has 909 main and 393 nested objects; only owners 243, 258, 260 and 261 change. This is bounded to the inspected pin, inventories and contexts, not all AI literature or saturation. Owner 260 retains its historical AntNet discovery URL; the inspected primary method is separately identified. Probability-vector resemblance neither makes this algorithm an automata synonym nor resolves automata theorem or variant scope.

Claims & Grounded Evidence

5 Claims
Scope #telephone-model
The source models a circuit-switched telephone network as an undirected graph. Each switching node has a finite capacity for simultaneous calls, while links have unbounded capacity in the model. A placed call consumes capacity at every node along its route; if a route node has no spare capacity, the call fails to be placed. This is the model in which the reported ant-based controller is evaluated.
Mechanism #source-directed-pheromone
Each node stores a probability row over its neighbors for every other possible destination. An ant has a source and a randomly chosen destination, and samples its next neighbor using the row for that destination. On arrival at a node, it reinforces the entry pointing to its previous neighbor in the row for its own source. Thus the row used for movement differs from the row updated by that ant. The ant terminates when it reaches its destination.
Mechanism #normalized-row-update
For a probability row p, a selected previous-neighbor entry j and finite raw deposit d>=0, the source update is p_new[j]=(p[j]+d)/(1+d), with p_new[k]=p[k]/(1+d) for k!=j. The row remains nonnegative and sums to one in exact arithmetic. Equivalently, p_new=(1-r)p+r e_j with r=d/(1+d). The selected entry’s net increase is d*(1-p[j])/(1+d), rather than the raw deposit d.
Mechanism #age-and-congestion
The controller weakens positive reinforcement as an ant ages and delays ants at congested nodes. Waiting both reduces near-term ant departures and makes later updates weaker through increased age. The reported parameterization uses d=0.08/age+0.005 for positive elapsed age and delay=floor(80*exp(-0.075*s)), where s is spare node capacity in percent. These are the reported simulation parameters; the general discussion permits other age and utilization responses.
Mechanism #calls-and-exploration
A new call chooses at each node the neighbor with the largest entry in the row for its destination, while ants use stochastic choices. The reported setup initializes equal neighbor probabilities and runs an ant-only period before introducing calls. An optional noise factor f lets an ant make a purely random choice with probability f and use the pheromone row with probability 1-f, encouraging exploration of alternatives. This noise changes ant movement, not the call argmax rule.

⚠️ Curated Limitations & Negative Results

Artificial Intelligence Ontology (AIO) Grounding

Mapped AIO Classes
AIO Target Class Mapping Relation Mapping Rationale
AIO:ReinforcementLearning
Reinforcement Learning
related_match The pinned task emphasizes feedback-driven exploration and exploitation without labeled corrective pairs. Ant-based telephone routing adapts route probabilities from ant age and congestion and can deliberately explore alternatives, supporting this qualified feedback-adaptation relationship. The local subject is a distributed routing algorithm, not every reinforcement-learning task. This is curator classification, not equivalence, an author-declared MDP or Q-learning formulation, or a convergence theorem; the report characterizes its motivating ant process as self-organization.

Evidence Sources & Primary Literature

1 Source
Source ID Publication / Document Type / Access Reference & Checksum
schoonderwoerd1996-hpl
Ruud Schoonderwoerd, Owen Holland, Janet Bruten, Leon Rothkrantz
author technical report
full_text
ID: HPL-96-76
sha256:8e9a08a92223…

Curation Provenance

Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-15T13:24:46Z
Updated: 2026-10-15T13:24:46Z
Source File: data/curated/AntBasedTelephoneRouting.yaml