Interpreting Knowledge-based Programs

Implementing Knowledge-Based Programs

Reasoning about multi-agents systems at the knowledge level allows us to abstract away from many concrete details of the systems we are considering. Fagin et al. introduced two notions to facilitate designing and reasoning about systems in terms of knowledge. The first notion is that of knowledge-basedprograms. Knowledge-based programs are defined as syntactic objects: programs with tests for k...

Probabilistic Knowledge-Based Programs

We introduce Probabilistic Knowledge-Based Programs (PKBPs), a new, compact representation of policies for factored partially observable Markov decision processes. PKBPs use branching conditions such as if the probability of φ is larger than p, and many more. While similar in spirit to valuebased policies, PKBPs leverage the factored representation for more compactness. They also cope with more...

Interpreting Bayesian Logic Programs

Various proposals for combining first order logic with Bayesian nets exist. We introduce the formalism of Bayesian logic programs, which is basically a simplification and reformulation of Ngo and Haddawys probabilistic logic programs. However, Bayesian logic programs are sufficiently powerful to represent essentially the same knowledge in a more elegant manner. The elegance is illustrated by th...

Interpreting Bayesian Logi Programs

Knowledge-Based Programs as Plans

Knowledge-based programs (KBPs) are high-level protocols describing the course of action an agent should perform as a function of its knowledge. The use of KBPs for expressing action policies in AI planning has been surprisingly underlooked. Given that to each KBP corresponds an equivalent plan and vice versa, KBPs are typically more succinct than standard plans, but imply more online computati...