A Syntactic Approach to Revising Epistemic States with Uncertain Inputs

Revising its beliefs when receiving new information is an important ability of any intelligent system. However, in realistic settings the new input is not always certain. A compelling way of dealing with uncertain input in an agent-based setting is to treat it as unreliable input, which may strengthen or weaken the beliefs of the agent. Recent work focused on the postulates associated with this form of belief change and on finding semantical operators that satisfy these postulates. In this paper we propose a new syntactic approach for this form of belief change and show that it agrees with the semantical definition. This makes it feasible to develop complex agent systems capable of efficiently dealing with unreliable input in a semantically meaningful way. Additionally, we show that imposing restrictions on the input and the beliefs that are entailed allows us to devise a tractable approach suitable for resource-bounded agents or agents where reactiveness is of paramount importance.

Incorporating PGMs into a BDI Architecture

In this paper, we present a hybrid BDI-PGM framework, in which PGMs (Probabilistic Graphical Models) are incorporated into a BDI (belief-desire-intention) architecture. This work is motivated by the need to address the scalability and noisy sensing issues in SCADA (Supervisory Control And Data Acquisition) systems. Our approach uses the incorporated PGMs to model the uncertainty reasoning and decision making processes of agents situated in a stochastic environment. In particular, we use Bayesian networks to reason about an agent’s beliefs about the environment based on its sensory observations, and select optimal plans according to the utilities of actions defined in influence diagrams. This approach takes the advantage of the scalability of the BDI architecture and the uncertainty reasoning capability of PGMs. We present a prototype of the proposed approach using a transit scenario to validate its effectiveness.

Can(Plan)+: Extending the Operational Semantics of the BDI Architecture to deal with Uncertain Information

The BDI architecture, where agents are modelled based on their beliefs, desires and intentions, provides a practical approach to develop large scale systems. However, it is not well suited to model complex Supervisory Control And Data Acquisition (SCADA) systems pervaded by uncertainty. In this paper we address this issue by extending the operational semantics of Can(Plan) into Can(Plan)+. We start by modelling the beliefs of an agent as a set of epistemic states where each state, possibly using a different representation, models part of the agent's beliefs. These epistemic states are stratified to make them commensurable and to reason about the uncertain beliefs of the agent. The syntax and semantics of a BDI agent are extended accordingly and we identify fragments with computationally efficient semantics. Finally, we examine how primitive actions are affected by uncertainty and we define an appropriate form of lookahead planning.

Probabilistic Planning in AgentSpeak using the POMDP framework.

AgentSpeak is a logic-based programming language, based on the Belief-Desire-Intention (BDI) paradigm, suitable for building complex agent-based systems. To limit the computational complexity, agents in AgentSpeak rely on a plan library to reduce the planning problem to the much simpler problem of plan selection. However, such a plan library is often inadequate when an agent is situated in an uncertain environment. In this paper, we propose the AgentSpeak+ framework, which extends AgentSpeak with a mechanism for probabilistic planning. The beliefs of an AgentSpeak+ agent are represented using epistemic states to allow an agent to reason about its uncertain observations and the uncertain effects of its actions. Each epistemic state consists of a POMDP, used to encode the agent’s knowledge of the environment, and its associated probability distribution (or belief state). In addition, the POMDP is used to select the optimal actions for achieving a given goal, even when facing uncertainty.

Choosing to engage and choosing to persist: The role of non-specific factors in health-promoting activities

Objective
Non-specific factors play an important role in determining benefits from health-promoting activities. Previous studies have focussed on beneficial outcomes of motivation during engagement. There are two aims of this project. First, we investigated whether expectancy and intrinsic motivation influence people's decisions to engage with health-promoting activities in the first instance and then subsequently adhere to them. Second, we examined the effects of providing information on health-promoting activities as a method of influencing expectancy and intrinsic motivation.

Method
In two studies, participants were informed about a health-promoting activity (Study 1: A breathing exercise for well-being; Study 2: A gratitude exercise for smoking cessation) and told that it has either a ‘known’ or ‘unknown’ effectiveness. Participants were then given the opportunity to engage with the activity over the following days. Expectancy and intrinsic motivation were measured after reading the information and prior to engagement with the activity. Adherence to the activity was measured at follow-up.

Results
In both studies, intrinsic motivation positively predicted willingness to engage with the activities as well as subsequent adherence. Expectancy predicted adherence in Study 1 and choices to engage in Study 2, but not after controlling for intrinsic motivation. Describing the gratitude exercise as having a known effectiveness in Study 2 enhanced motivation and adherence to the activity.

Conclusions
The non-specific benefit brought by intrinsic motivation plays an important role in choosing to engage with health-promoting activities as well as subsequent adherence. Our results also show that simple statements about the potential benefits of a health-promoting activity can motivate engagement and adherence.