Reasoning about Cognitive Trust in Stochastic Multiagent Systems

We consider the setting of stochastic multiagent systems modelled as stochastic multiplayer games and formulate an automated verification framework for quantifying and reasoning about agents’ trust. To capture human trust, we work with a cognitive notion of trust defined as a subjective evaluation that agent A makes about agent B’s ability to complete a task, which in turn may lead to a decision by A to rely on B. We propose a probabilistic rational temporal logic PRTL*, which extends the probabilistic computation tree logic PCTL* with reasoning about mental attitudes (beliefs, goals, and intentions) and includes novel operators that can express concepts of social trust such as competence, disposition, and dependence. The logic can express, for example, that “agent A will eventually trust agent B with probability at least p that B will behave in a way that ensures the successful completion of a given task.” We study the complexity of the automated verification problem and, while the general problem is undecidable, we identify restrictions on the logic and the system that result in decidable, or even tractable, subproblems

Distributed Optimisation in Wireless Sensor Networks: A Hierarchical Learning Approachs

Ph.DDOCTOR OF PHILOSOPH

Complexity Issues in Markov Decision Processes

We survey the complexity of computational problems about Markov decision processes: evaluating policies, finding good and best policies, approximating best policies, and related decision problems. 1 Introduction Partially-observable Markov decision processes (POMDPs) model sequential decision making when outcomes are uncertain and the state of the system cannot be completely observed. They consist of decision epochs, states, observations, actions, transition probabilities, and rewards. At each decision epoch, the process is in some state, from which a "signal" is sent out which can be observed from outside. (Note that different states may send equal signals.) Choosing an action in a state generates a reward or possibly a cost (negative reward) and determines the state at the next decision epoch through a transition probability function. Policies are prescriptions of which action to take under any eventuality (i.e. any sequence of observations made in the previous decision epochs). De..