Alex Kirsch
Independent Scientist
DE | EN
Research

How does thinking work? (How) can we instruct machines to think? And how can we use our knowledge to make machines more useful for people?

I am currently mostly pondering about the abililty of humans to make decisions in everyday life. I use results from psychology and behavioral economics to implement computational decision processes that resemble those of humans. The goal is not do much to get improve automatic responses, but more to better support human activities with computers.

Research Interests

Cognitive Systems

Intelligence is not an isolated phenomenon. The only intelligence we know about lives inside human bodies, not just the brains. As humans we are embedded into our environment and instead of just reacting to it, as most of the living world around us does, we think about abstract concepts, envision worlds that are not and can never be, and communicate and collaborate with one another. Cognitive Systems are arguably the hardest part of AI (for me they constitute AI), but that also makes them a most fascinating object of study.

Decision-Making

In everyday life people master comlex decisions under uncertainty and time pressure. As soon as organizations solve tasks it seems these capabilities are largely suppressed by rigid processes that are designed contrary to what people do well. From my research on human and automatic decision making I transfer processes that people use successfully in everyday life to organizational routines. This corresponds roughly the trend of agile software development, but it is much more general.

Human-Centered AI

Whether artificial intelligence is presented as a dream or a nightmare, the computer is always the focus. But what if you cleverly combine technical possibilities with human abilities, if you look at people and computers as an integral system? In my opinion, every system that is supposed to be suitable for everyday use must be designed with the user interface as a starting point, to be complemented gradually with intelligent tools.

Projects

since 2021

todoListo: Your personal task management tool

at Alex Kirsch IT GmbH
Prototyping
User Experience Design
Clojure
ClojureScript
React
Keycloak
Link to todoListo

A full software developed in one hand, from concept via software-prototyping to a complete product.

Impressions

Task view of todoListoAssigning properties in todoListo
Notes view of todoListo

The Development of todoListo

2021
2022
2023
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Idea and start of prototyping
First test release for daily use
First external test users
End of prototyping phase, refining of concept
Start of (re-)implementation
Design of todoListo Website
First tests with server backend
Payment and Account Management
First public release
2021 – 2023

Transformation consulting and support for an online publisher of medical knowledge

Consulting
User Experience Design
Business analysis
Requirements Analysis
Workshops
Prototyping
ClojureScript
JavaScript
React

The 10-year-old company has been very successful with quick innovations in its online products. By focusing on innovation, technical debt has been accumulated both on a conceptual and on an architectural level, which led to declining innovative power. In this project, interal software tools are to be reconceptualized and reimplemented, while ensuring that day-to-day business is not impaired. Ideally, new functionality that directly improve the end user product, should be added in the course of the transformation process.

My Role

  • Analysis and illustration of hitherto existing system functionality and shortcomings
  • Preparation and presentation of information for management to argue the need for the transformation
  • Information and onboarding of different stakeholders (end users, developers, product designers)
  • Planning of intermediate goals and project steps
  • User experience research
  • Research of central libraries
  • Functional prototyping of a web frontend

Peer Feedback

Feedback I received from project collaborators:

  • very structured and well prepared for meetings
  • an iterative approach that can deal with uncertainty and gets closer to a clear picture on every iteration
  • great presenter of results and insights
  • inspiring with new angles on old issues
  • not only capable on a concept level but also a quick prototyper
  • great user-centric end-to-end thinking

2021
Digitalisation strategy for a logistics SME
Consulting
Business analysis
Requirements Analysis
Workshops
Market research
Roadmap
Logistics
Project management
2019 – 2021
Sort-it: a demonstration prototype for a knowledge tool
Categorisation
Decision processes
Cognition
User Experience Design
Artificial Intelligence
Prototyping
Agile software development
ClojureScript
React
Project management
2012 – 2018

Human-Centered Artificial Intelligence

Young Scholar's Programme of the Bavarian Academy of Sciences and Humanities
Decision processes
Cognition
Autonomous robots
Clojure
JavaFX

Human-Centered Artificial Intelligence is an interdisciplinary effort to use artificial intelligence as a tool for better human-robot (or more generally human-computer) interaction. It comprises user-centered development of the functionality of technical systems, while at the same time using inspiration from biological systems to make technology more robust for real-world applications.

Principles
Propose Illustration of decision alternatives
Decisions, both by humans and machines, are based on alternatives. In contrast to conventional AI methods, the alternatives in my algorithm are not predefined, but are developed dynamically during the decision-making process.
Evaluate Illustration of the evaluation of decision alternatives
In my model, alternatives are assessed by independent evaluation processes. These represent different aspects of solution quality and may contradict each other.
Focus Illustration of consolidation and focus on the best decision alternative
In common AI methods, different evaluation aspects are calculated into a value using weighted sums. The results are usually unintuitive, as humans do not form weighted sums when making decisions, which has been proven by empirical studies. My method imitates human heuristics using methods from social choice theory.
Iterate Illustraion of the iteration process between generation of alternatives, evalation and consolidation
By repeatedly generating and adapting alternatives, evaluating and focusing, the solution is determined step by step. The process can run fully automatically or in a collaborative process with humans.
Forschungsmethode

Wissenschaft ist ein iterativer Prozess von Beobachten und Theoriebildung. Genau so habe ich meinen Algorithmus entwickelt und genau so kann er für konkrete Fragestellungen genutzt werden. Durch das Protoyping von Anwendungsfällen bekommt man schnell ein Gefühl dafür was gut funktioniert und wo Verbesserungsbedarf besteht.

In dem Ansatz geht es nicht darum, eine Speziallösung für ein spezifisches Problem zu entwickeln, sondern einen allgemeinen Mechanismus, der auf viele verschiedene Aufgabenstellungen übertragbar ist. Deshalb ist das Verfahren an zwei sehr unterschiedlichen Aufgaben entwickelt worden: dem Traveling Salesperson Problem (einem Problem aus der theoretischen Informatik, das in vielen realen Aufgaben in abgewandelter Form vorkommt) und der Navigation von autonomen Robotern (d.h. der Roboter hat die Aufgabe einen Zielpunkt zu erreichen und muss dafür den nächsten Befehl an seine Räder schicken).

Ansichten der verschiedenen Abstraktionsstufen bei der Roboternavigation

Das Verfahren eignet sich vor allem für Beinahe-Optimierungsprobleme, also Aufgaben, die sich nicht vollständig formalisieren lassen, die jedoch zu unübersichtlich sind um von Menschen ohne technische Unterstützung gelöst zu werden, z.B.

  • Erstellung von Produktionsplänen
  • Kategorisierung von Produktgruppen
  • Planung von Verkaufsniederlassungen
  • Bildung von (Kunden-)Kohorten
  • Wartung komplexer IT-Systeme

Im Gegensatz zu klassischen Optimierungsverfahren zeichnet sich mein Ansatz aus durch

  • hohe Nutzerakzeptanz
  • Robustheit
  • Verständlichkeit

Veröffentlichungen
Alexandra Kirsch. A Unifying Computational Model of Decision Making. Cognitive Processing, 20(2), pp. 243 – 259, 2019. [pdf from HAL]
Alexandra Kirsch. Lessons from Human Problem Solving for Cognitive Systems Research. Advances in Cognitive Systems, 5, pp. 13 – 24, 2017.
Alexandra Kirsch. A Modular Approach of Decision-Making in the Context of Robot Navigation in Domestic Environments. In: 3rd Global Conference on Artificial Intelligence (GCAI), pp. 134 – 147. Miami, Fl., USA, 2017. [pdf from HAL]
Alexandra Kirsch. Heuristic Decision-Making for Human-aware Navigation in Domestic Environments. In: 2nd Global Conference on Artificial Intelligence (GCAI), pp. 200 – 213. Berlin, Germany, 2016. [pdf from HAL]
Tim Rach, Alexandra Kirsch. Modelling human problem solving with data from an online game. Cognitive Processing, 17(4), pp. 415 – 428, 2016. [pdf from HAL]
Alexandra Kirsch. Heuristic Problem Solving with Abstract Knowledge in the Context of the Travelling Salesperson Problem. 2014. (unpublished article) [pdf from HAL]
Alexandra Kirsch. Hierarchical Knowledge for Heuristic Problem Solving — A Case Study on the Traveling Salesperson Problem. In: First Annual Conference on Advances in Cognitive Systems (ACS). 2012. [pdf from HAL]
Alexandra Kirsch. Humanlike Problem Solving in the Context of the Traveling Salesperson Problem. In: AAAI Fall Symposium on Advances in Cognitive Systems. AAAI Press, 2011. [pdf from HAL]

Publications

Papenmeier, Frank, Purcalla Arrufi, Juan, Kirsch, Alexandra. Stories in the Mind? The Role of Story-Based Categorizations in Motion Classification. Cognitive Science, 47(9), pp. e13332, 2023.
Clemens Beckstein, Alexandra Kirsch. Suche. In: Handbuch der Künstlichen Intelligenz. Ed: Günther Görz, Ute Schmid, Tanya Braun, 6th edition. De Gruyter. Chap. 3. 2021.
Alexandra Kirsch. Shakey Ever After? Questioning Tacit Assumptions in Robotics and Artificial Intelligence. Künstliche Intelligenz, 33(4), pp. 423 – 428, 2019. [pdf from HAL]
Alexandra Kirsch. A Unifying Computational Model of Decision Making. Cognitive Processing, 20(2), pp. 243 – 259, 2019. [pdf from HAL]
Frank Papenmeier, Meike Uhrig, Alexandra Kirsch. Human Understanding of Robot Motion: The Role of Velocity and Orientation. International Journal of Social Robotics, 11(1), pp. 75 – 88, 2019. [pdf from HAL]
Alexandra Kirsch. Explain to whom? Putting the User in the Center of Explainable AI. In: Proceedings of the First International Workshop on Comprehensibility and Explanation in AI and ML 2017, co-located with 16th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2017). Bari, Italy, 2017. (invited paper) [pdf from HAL]