Hendrik Skubch Books

The book delves into FLUX, a programming system that empowers reasoning agents to maintain an internal model of their environment, allowing them to handle partial knowledge and engage in advanced reasoning techniques like planning. It emphasizes the creation of adaptable strategies derived from experience and domain knowledge, enabling agents to address unpredictable real-world challenges. The authors introduce a method for breaking down complex problems into manageable subproblems using heuristics, leading to the development of subprograms through various reasoning techniques, although the application remains limited to simulated environments.

As research progresses, it enables multi-robot systems to be used in more and more complex and dynamic scenarios. Hence, the question arises how different modelling and reasoning paradigms can be utilised to describe the intended behaviour of a team and execute it in a robust and adaptive manner. Hendrik Skubch presents a solution, ALICA (A Language for Interactive Cooperative Agents) which combines modelling techniques drawn from different paradigms in an integrative fashion. Hierarchies of finite state machines are used to structure the behaviour of the team such that temporal and causal relationships can be expressed. Utility functions weigh different options against each other and assign agents to different tasks. Finally, non-linear constraint satisfaction and optimisation problems are integrated, allowing for complex cooperative behaviour to be specified in a concise, theoretically well-founded manner.

FLUX is a programming system for reasoning agents based on the fluent calculus. FLUX agents maintain an internal model of their environment, enabling them to represent partial knowledge. This ability gives rise to powerful reasoning techniques such as planning. Conventionally, the strategy such an agents executes in order to achieve a certain goal is static and needs to be defined in advance. Agents which are able to derive a strategy on their own, using experience and domain knowledge, can provide be a way to tackle real world problems that change unexpectedly or for which no solution is known in advance. In this work, we focus on learning a strategy able to use the reasoning capabilities offered by systems such as FLUX. We present a technique to decompose a problem into smaller subproblems using heuristics derived from a given domain description. On this basis, subprograms are learnt in different hypothesis spaces, which correspond to different reasoning techniques. With the help of a grammar inducer, an inductive inference machine composes the found subprograms into the final strategy. The application of this work is still restricted to scenarios where a simulator is available.