this document is its second year report.

Conformant planning is the problem of nding a sequence of actions that is guaranteed to achieve the goal for any possible initial state and nondeterministic behavior of the planning domain. In this paper we present a new approach to conformant planning. We propose an algorithm that returns the set of all conformant plans of minimal length if the problem admits a solution, otherwise it returns with failure. Our work is based on the planning via model checking paradigm, and relies on symbolic techniques such as Binary Decision Diagrams to compactly represent and eciently analyze the planning domain. The algorithm, called cmbp, has been implemented in the mbp planner. cmbp is strictly more expressive than the state of the art conformant planner cgp. Furthermore, an experimental evaluation suggests that cmbp is able to deal with uncertainties more eciently than cgp.

In this paper, we explore an architecture, called K-Trek, that enables mobile users to travel across knowledge distributed over a large geographical area (ranging from large public buildings to a national park). Our aim is providing, distributing, and enriching the environment with location-sensitive information for use by agents on board of mobile and static devices. Local interactions among KTrek devices and the distribution of information in the larger environment adopt some typical peer-to-peer patterns and techniques. We introduce the architecture, discuss some of its potential knowledge management applications, and present a few experimental results obtained with simulation.

Multi-agent systems are often taken as a paradigm in the speci cation of complex systems because of their power in the abstraction from implementation details. Dierent kinds of modal logics have been used to model agents' knowledge - beliefs - desires, and their evolution over time. In this report I investigate how model checking techniques can be applied to some problems of veri cation in multi-agent systems. I review briey th literature for multi-agent systems logics, and for model checking.

We present a model checking method which greedily explores the state space. Using ideas similar to greedy satisfiability checking, our method tries to fit a path to match a temporal specification. The advantages of this method is that we do not need any quantifications, we do not calculate a reachable (neither forward nor backward) set of states, and the memory requirements are quite small.