By Hendrik Skubch

As learn progresses, it permits multi-robot structures for use in additional and extra complicated and dynamic situations. for that reason, the query arises how various modelling and reasoning paradigms could be utilised to explain the meant behaviour of a workforce and execute it in a strong and adaptive demeanour. Hendrik Skubch provides an answer, ALICA (A Language for Interactive Cooperative brokers) which mixes modelling strategies drawn from diverse paradigms in an integrative model. Hierarchies of finite country machines are used to constitution the behaviour of the workforce such that temporal and causal relationships might be expressed. application features weigh diverse ideas opposed to one another and assign brokers to diverse initiatives. ultimately, non-linear constraint delight and optimisation difficulties are built-in, bearing in mind advanced cooperative behaviour to be laid out in a concise, theoretically well-founded manner.

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To allow agents to select a plan from a set of possible alternatives through comparison, we group plans together in what we call plantypes. Such grouped plans can be (but are not necessarily) directed towards the same goal. The set of all plantypes in a pALICA program is referred to as P∨ ⊆ 2P . A plan p belonging to plantype P is also called a realisation of P. Fostering the idea of hierarchies, we introduce these sets as properties of states. Thus, each state can contain a number of sets of alternative plans in the same manner as it can contain behaviours.

XABSL describes agent behaviour through a hierarchical structure of states and options. Zweigle et al. [187] expanded this approach with a petri-netlike structure to capture multi-agent interaction. The graphically modelled XPlM Nets were compiled into XABSL trees. Although the general idea of a hierarchical interaction net is central to ALICA as well, there are fundamental differences to XPlM Nets. Most importantly, ALICA describes agents using capabilities and implements a double-layered abstraction between plans and agents through the use of roles and tasks.

13 introduces a method for describing such dependencies and thereby coordinating different activities within a plan. For now, we introduce the notion of a task, which points to an initial state within a plan, and thus identifies a sub-automaton within it. The set of all tasks of a program is referred to as T . Note that while we do not enforce the sub-automata identified by each task to be disjoint, disjoint subautomata are beneficial, since in this case each state unambiguously identifies a task. 5).

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