# Project Presentation: Modelling Navigtion and Landmarking in Ants Project by Daniel Keer Supervised by Dr Steve Russ Abstract: An attempt to model some.

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Project Presentation: Modelling Navigtion and Landmarking in Ants Project by Daniel Keer Supervised by Dr Steve Russ Abstract: An attempt to model some of the ways ants navigate, particularly focusing on their visual navigation methods and use of landmarks. The project has been undertaken from an Empirical Modelling perspective, using the modelling tool tkeden.

Project Aims Broadly: To model the navigational aspects of ant behaviour, from an Empirical Modelling perspective. I wanted there to be a strong correlation with real scientific experiments and understanding. I wanted to make the model as visual as possible, and for the workings of the AI to be transparent (in keeping with the Empirical Modelling perspective).

Ants The ant problem: to forage for food and return to the nest as quickly and effectively as possible. Ants have a range of sensory abilities and orientation methods that they use to do this (varying from species to species of course) I will describe some of these briefly so that you can understand the model I chose ants as a subject for the project because of the varied and quite amazing way that they navigate.

PATH INTGRATION Ants can keep track of the distance and direction of their return path to the nest when out foraging. This ability is called Path Integration. However their calculations are based on rough estimations (not integration), and are not totally reliable. My model implements a calculation method that ants are thought to use, and makes clear the discrepancy with the exact values.

VISION Most species of ants have a keen sense of vision. In the model, I have divided the way they use sight into two distinct ways: 1.To recognise objects of interest nearby – food, enemies, nestmates, etc. 2.To recognise landmarks – taller objects such as trees, rocks, etc. Adjacency view Obstacle view

VISION – LANDMARKING & SNAPSHOTS Many ant species are capable of remembering snapshots, which help them to remember the route to food. A snapshot is thought to be a representation of surrounding landmarks (focusing on the colour and elevation of tall objects). The procedure I will describe next is a simplified view of how ants combine their Path Integration and Snapshot abilities to navigate to food sources. Snapshot memory in the model

COMBINED ANT NAVIGATION SCENARIO The ant starts off at the nestIt will then begin a (fairly random) search for food……and may eventually be lucky enough to find some The ant will then head in the direction that its Path Integration record tells it is the way back to the nest. After a set number of steps, the ant will stop, turn 180 o and take a snapshot. It will fix any nearby objects in its memory. This will be repeated several times on the way back to the nest.At the nest, it will deposit its food. It will then try to find its way back to the food source. It will compare the snap in memory to surroundings and move that way. When the match is good enough, it will move onto the next shot in memory, and will do the same again. In this way, it can approximately relocate the food source

Specific Project Aim The scenario just described is an interesting problem in AI. Modelling this type of complex behaviour became a main aim of the project. Two major components are necessary for this: A suitable environment and interface (landmarks, ant, food, senses, etc) The AI for navigation

Basis for the Environment (K.C. Tans Model) This Original Model Provided: Toolbar A Clock Basic map Ability to place blocks Ranger Ant and rays Food and scent (which I removed) I was lucky enough not to have to start from scratch with the environment. The environment that I developed was built on top of a previous project by K.C. Tan. This saved me a lot of time, and meant that I could implement some interesting features.

Basis for the Environment (K.C. Tans Model) The changes that I have made to this original will be evident during the demonstration.

For the AI, I implemented a set of AI modes that correlate with what the ant does under different situations – search, return to nest, etc. The ant will switch between these modes under certain circumstances (e.g. if it finds food). The mode that the ant is currently in is clearly displayed in the interface. At Nest Locate Nest Search Return (Simple) Return (Take Snapshots) Pick Up Food Travel Limit Exceeded Nest Found Close (By P.I.) Initiate Search AI MODES Pick Up Food Follow Route Food Found Trail to Follow Lost

At Nest Locate Nest Search Return (Simple) Return (Take Snapshots) Pick Up Food Travel Limit Exceeded Nest Found Close (By P.I.) Initiate Search AI MODES Pick Up Food Follow Route Food Found Trail to Follow Lost

At Nest Locate Nest Search Return (Simple) Return (Take Snapshots) Pick Up Food Travel Limit Exceeded Nest Found Close (By P.I.) Initiate Search AI MODES Pick Up Food Follow Route Food Found Trail to Follow Lost

At Nest Locate Nest Search Return (Simple) Return (Take Snapshots) Pick Up Food Travel Limit Exceeded Nest Found Close (By P.I.) Initiate Search AI MODES Pick Up Food Follow Route Food Found Trail to Follow Lost

At Nest Locate Nest Search Return (Simple) Return (Take Snapshots) Pick Up Food Travel Limit Exceeded Nest Found Close (By P.I.) Initiate Search AI MODES Pick Up Food Follow Route Food Found Trail to Follow Lost

At Nest Locate Nest Search Return (Simple) Return (Take Snapshots) Pick Up Food Travel Limit Exceeded Nest Found Close (By P.I.) Initiate Search AI MODES Pick Up Food Follow Route Food Found Trail to Follow Lost

KEY ACHIEVEMENTS IN THE AI IN THE MODEL: The ant is able to record snapshots into memory. It can then follow the route back, comparing the snapshot with what it can currently see to decide how to turn. It is usually quite successful at this. This is the key achievement of my project.

Project Management DESIGN PROCESS: I had a clear idea of what I should include in the environment/interface, and developed KCs model accordingly. I had some ideas about how I might do parts of the AI, and I experimented with the model to see how they worked in practice. I built up a complete working AI through this interaction.

Project Management TIME MANAGEMENT: I have set targets for completing sections. I have kept up a consistent pace of work by allocating spare time to the project regularly As a result I have managed to finish most of what I wanted to within the time.

Project Management PROBLEMS: No major upsets The biggest difficulty has been at the end of the project, trying to implement the snapshot following part of the AI This took a lot more work than I was expecting, and I have not had time for all the testing and fine-tuning that I might ideally have liked

Empirical Modelling Approach to AI I think that the Empirical Modelling experimental approach has significant merit for AI development. I think it would have been very difficult to come up with a realistic AI for this problem without actually playing with the model in the way I did. However, this way of working has resulted in the AI code being cobbled together as I went along. Once I had functionality working, I had to spend significant time re-structuring the code because it was a mess.

Possible Future Extensions A Multi-Ant Model (looking at group behaviours) would be very interesting, though there may be speed issues. Investigate other navigation methods – scent, magnetic compass, other landmarking methods, touch?? Genetic-type Evolution of an AI - I have come across some examples of this in my research, and it is a fascinating concept Having adapted a model myself, I would be very pleased for my model to be further extended in the future. There are many ways that this could be done…

GENERAL CONCLUSIONS I regard the project as a success, as I have accomplished what I set out to do The environment and interface that I have built up would be a useful tool for anyone wishing to simulate insect behaviour I believe that the AI that I have developed generates some very interesting results Empirical Modelling has significant merit as an approach to AI design

- END OF PRESENTATION - Thank you for listening. I will now demonstrate the model working. (5 Minutes will be allocated at the end for questions, but please feel free to stop me during demonstration)

- END OF PRESENTATION - Thank you for listening. I will now demonstrate the model working. (5 Minutes will be allocated at the end for questions, but please feel free to stop me during demonstration)

( EXTRA UNUSED SLIDES )

POLARISATION COMPASS Ants can detect the presence of polarised light in the sky (from sun, moon, stars, etc), and can orient themselves with this. Representation in the model: sun facing nest

Path Integration When Foraging Black Black – outward path. red red – return path return You can see from this example foraging walk that an ants return path is accurate and (fairly) straight-line. outward This is despite a long and rambling outward search path.

At Nest Locate Nest Search Return (Simple) Return (Take Snapshots) Pick Up Food Travel Limit Exceeded Nest Found Close (By P.I.) Initiate Search AI MODES Pick Up Food Follow Route Food Found Trail to Follow Lost

At Nest Locate Nest Search Return (Simple) Return (Take Snapshots) Pick Up Food Travel Limit Exceeded Nest Found Close (By P.I.) Initiate Search AI MODES Pick Up Food Follow Route Food Found Trail to Follow Lost

At Nest Locate Nest Search Return (Simple) Return (Take Snapshots) Pick Up Food Travel Limit Exceeded Nest Found Close (By P.I.) Initiate Search AI MODES Pick Up Food Follow Route Food Found Trail to Follow Lost

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