Hybrid Cloud Model Checking Using the Interaction Layer of HARMS for Ambient Intelligent Systems

Soon, humans will be co-living and taking advantage of the help of multi-agent systems in a broader way than the present. Such systems will involve machines or devices of any variety, including robots. These kind of solutions will adapt to the special needs of each individual. However, to the concern of this research effort, systems like the ones mentioned above might encounter situations that will not be seen before execution time. It is understood that there are two possible outcomes that could materialize; either keep working without corrective measures, which could lead to an entirely different end or completely stop working. Both results should be avoided, specially in cases where the end user will depend on a high level guidance provided by the system, such as in ambient intelligence applications. This dissertation worked towards two specific goals. First, to assure that the system will always work, independently of which of the agents performs the different tasks needed to accomplish a bigger objective. Second, to provide initial steps towards autonomous survivable systems which can change their future actions in order to achieve the original final goals. Therefore, the use of the third layer of the HARMS model was proposed to insure the indistinguishability of the actors accomplishing each task and sub-task without regard of the intrinsic complexity of the activity. Additionally, a framework was proposed using model checking methodology during run-time for providing possible solutions to issues encountered in execution time, as a part of the survivability feature of the systems final goals

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks

A general approach to temporal reasoning about action and change

Reasoning about actions and change based on common sense knowledge is one of the most important and difficult tasks in the artificial intelligence research area. A series of such tasks are identified which motivate the consideration and application of reasoning formalisms. There follows a discussion of the broad issues involved in modelling time and constructing a logical language. In general, worlds change over time. To model the dynamic world, the ability to predict what the state of the world will be after the execution of a particular sequence of actions, which take time and to explain how some given state change came about, i.e. the causality are basic requirements of any autonomous rational agent. The research work presented herein addresses some of the fundamental concepts and the relative issues in formal reasoning about actions and change. In this thesis, we employ a new time structure, which helps to deal with the so-called intermingling problem and the dividing instant problem. Also, the issue of how to treat the relationship between a time duration and its relative time entity is examined. In addition, some key terms for representing and reasoning about actions and change, such as states, situations, actions and events are formulated. Furthermore, a new formalism for reasoning about change over time is presented. It allows more flexible temporal causal relationships than do other formalisms for reasoning about causal change, such as the situation calculus and the event calculus. It includes effects that start during, immediately after, or some time after their causes, and which end before, simultaneously with, or after their causes. The presented formalism allows the expression of common-sense causal laws at high level. Also, it is shown how these laws can be used to deduce state change over time at low level. Finally, we show that the approach provided here is expressive

Verifying requirements for resource-bounded agents

This thesis presents frameworks for the modelling and verification of resource-bounded reasoning agents. The resources considered include the time, memory, and communication bandwidth required by agents to achieve a goal. The scalability and expressiveness of standard model checking techniques is investigated using two typical multiagent reasoning problems which can be easily parameterised to increase or decrease the problem size. Both a complexity analysis and experimental results suggest that reasonably sized problem instances are unlikely to be tractable for a standard model checker without steps to reduce the branching factor of the state space. We propose two approaches to address this problem: the use of abstract specifications to model the behaviour of some of the agents in the system, and exploiting information about the reasoning strategy adopted by the agents. Abstract specifications are given as Linear Temporal Logic (LTL) formulae which describe the external behaviour of the agents, allowing their temporal behaviour to be compactly modelled. Conversely, reasoning strategies allow the detailed specification of the ordering of steps in the agent’s reasoning process. Both approaches have been combined in an automated verification tool TVRBA for rule-based multi-agent systems which allows the designer to specify information about agents’ interaction, behaviour, and execution strategy at different levels of abstraction. The TVRBA tool generates an encoding of the system for the Maude LTL model checker, allowing properties of the system to be verified. The scalability of the new approach is illustrated using three case studies

Verifying requirements for resource-bounded agents

This thesis presents frameworks for the modelling and verification of resource-bounded reasoning agents. The resources considered include the time, memory, and communication bandwidth required by agents to achieve a goal. The scalability and expressiveness of standard model checking techniques is investigated using two typical multiagent reasoning problems which can be easily parameterised to increase or decrease the problem size. Both a complexity analysis and experimental results suggest that reasonably sized problem instances are unlikely to be tractable for a standard model checker without steps to reduce the branching factor of the state space. We propose two approaches to address this problem: the use of abstract specifications to model the behaviour of some of the agents in the system, and exploiting information about the reasoning strategy adopted by the agents. Abstract specifications are given as Linear Temporal Logic (LTL) formulae which describe the external behaviour of the agents, allowing their temporal behaviour to be compactly modelled. Conversely, reasoning strategies allow the detailed specification of the ordering of steps in the agent’s reasoning process. Both approaches have been combined in an automated verification tool TVRBA for rule-based multi-agent systems which allows the designer to specify information about agents’ interaction, behaviour, and execution strategy at different levels of abstraction. The TVRBA tool generates an encoding of the system for the Maude LTL model checker, allowing properties of the system to be verified. The scalability of the new approach is illustrated using three case studies

Re-design of compositional systems

Treur, J. [Promotor]Brazier, F.M.T. [Copromotor