MIDCA consists of
action-perception cycles at both the cognitive (i.e., object) level and the metacognitive (i.e., meta-) level (see Figure 1).
The output side of each cycle consists of intention, planning, and action execution,
whereas the input side consists of perception, interpretation, and goal evaluation. A cycle selects a goal and commits to achieving it.
The agent then creates a plan to achieve the goal and subsequently executes the planned actions to make the domain match the goal state.
The agent perceives changes to the environment resulting from the actions, interprets the percepts with respect to the plan, and evaluates the
interpretation with respect to the goal.
At the object level, the cycle achieves goals that change the environment (i.e., ground level). At the meta-level, the cycle achieves goals that change the object level.
That is, the metacognitive
perception components introspectively monitor the processes and mental state changes at the cognitive level.The
consists of a meta-level controller that mediates reasoning over an abstract representation of the object level cognition.
Kyudo is a Community question answering (cQA) platform for people with diverse background to share information and knowledge. The traditional FAQ archives constructed by the experts or companies where anyone can ask and answer questions on any topic, and people seeking information are connected to those who know the answers. In cQA, the systems can directly return answers to the queried questions instead of a list of relevant documents, thus providing an effective alternative to the traditional ad-hoc information retrieval.
In Kyudo we first build an easy-to-use thesaurus from Wikipedia, which explicitly derive the concept relationships based on the structural knowledge in Wikipedia, including synonymy, polysemy, hypernymy and associative relation. The thesaurus facilitates the integration of the rich world knowledge of Wikipedia into questions, because it resolves synonyms and introduces more general and associative concepts which may help identify related topics between the queried questions and the historical questions. Besides, it provides a rich context for polysemy concept sense disambiguation. Then we treat the different relations in the thesaurus according to their different importance, in order to improve the traditional similarity measure for question retrieval.