Leveraging New Plans in AgentSpeak(PL)

Many papers have been written on the anticancer properties of dietary flavonoids, and a range of potential mechanisms of action of flavonoids. However, most dietary flavonoids - notably polyphenolic flavonoids—have very poor ADME properties, and the levels necessary to stop growth of tumour cells cannot be sustained in a human body trough dietary intake alone. At present no flavonoid based drugs are clinically used in cancer therapy. Thus, whereas epidemiological and pre-clinical data seem to indicate a high potential for flavonoids, from the point of view of the pharmaceutical industry and drug developers, they are considered poor candidates. The flavones—which constitute a subgroup of the flavonoids—show some structural analogy with oestrogen and are known to interact with human oestrogen receptors, either as agonist or as antagonist. They are classed as phytoestrogens, and may play a role in cancer prevention through a mechanism of action possibly similar to that of the clinically used medication tamoxifen. Flavones are abundantly present in common fruits and vegetables, many of which have been associated with cancer prevention. Their phytoestrogen activity makes that they can assert their biological action at concentrations that are realistically achievable in the human systemic circulation

Social Continual Planning in Open Multiagent Systems: a First Study

Abstract. We describe a Multiagent Planning approach, named Social Continual Planning, that tackles open scenarios, where agents can join and leave the system dynamically. The planning task is not defined from a global point of view, setting a global objective, but we allow each agent to pursue its own subset of goals. We take a social perspective where, although each agent has its own planning task and planning algorithm, it needs to get engaged with others for accomplishing its own goals. Cooperation is not forced but, thanks to the abstraction of social commitment, stems from the needs of the agents

Resilience, reliability, and coordination in autonomous multi-agent systems

Acknowledgements The research reported in this paper was funded and supported by various grants over the years: Robotics and AI in Nuclear (RAIN) Hub (EP/R026084/1); Future AI and Robotics for Space (FAIR-SPACE) Hub (EP/R026092/1); Offshore Robotics for Certification of Assets (ORCA) Hub (EP/R026173/1); the Royal Academy of Engineering under the Chair in Emerging Technologies scheme; Trustworthy Autonomous Systems “Verifiability Node” (EP/V026801); Scrutable Autonomous Systems (EP/J012084/1); Supporting Security Policy with Effective Digital Intervention (EP/P011829/1); The International Technology Alliance in Network and Information Sciences.Peer reviewedPostprin

A Framework for Plan Library Evolution in BDI Agent Systems

The Belief-Desire-Intention (BDI) paradigm is a flexible framework for representing intelligent agents. Practical BDI agent systems rely on a static plan library to reduce the planning problem to the simpler problem of plan selection. However, fixed pre-defined plan libraries are unable to adapt to fast-changing environments pervaded by uncertainty. In this paper, we advance the state-of-the-art in BDI agent systems by proposing a plan library evolution architecture with mechanisms to incorporate new plans (plan expansion) and drop old/unsuitable plans (plan contraction) to adapt to changes in a realistic environment. The proposal follows a principled approach to define plan library expansion and contraction operators, motivated by postulates that clearly highlight the underlying assumptions, and quantified by decision-support measures of temporal information. In particular, we demonstrate the feasibility of the proposed contraction operator by presenting a multi-criteria argumentation based decision making to remove plans exemplified in a planetary vehicle scenario

DSOL: a declarative approach to self-adaptive service orchestrations

Service oriented computing (SOC) has brought a simplification in the way distributed applications can be built. Mainstream approaches, however, failed to support dynamic, self-managed compositions that would empower even non-technical users to build their own orchestrations. Indeed, because of the changeable world in which they are embedded, service compositions must be able to adapt to changes that may happen at run-time. Unfortunately, mainstream SOC languages, like BPEL and BPMN, make it quite hard to develop such kind of self-adapting orchestrations. We claim that this is mostly due to the imperative programming paradigm they are based on. To overcome this limitation we propose a radically different, strongly declarative approach to model service orchestration, which is easier to use and results in more flexible and self-adapting orchestrations. An ad-hoc engine, leveraging well-known planning techniques, interprets such models to support dynamic service orchestration at run-time

A BDI agent programming language with failure handling, declarative goals, and planning

Agents are an important technology that have the potential to take over contemporary methods for analysing, designing, and implementing complex software. The Belief- Desire-Intention (BDI) agent paradigm has proven to be one of the major approaches to intelligent agent systems, both in academia and in industry. Typical BDI agent-oriented programming languages rely on user-provided ''plan libraries'' to achieve goals, and online context sensitive subgoal selection and expansion. These allow for the development of systems that are extremely flexible and responsive to the environment, and as a result, well suited for complex applications with (soft) real-time reasoning and control requirements. Nonetheless, complex decision making that goes beyond, but is compatible with, run-time context-dependent plan selection is one of the most natural and important next steps within this technology. In this paper we develop a typical BDI-style agent-oriented programming language that enhances usual BDI programming style with three distinguished features: declarative goals, look-ahead planning, and failure handling. First, an account that mixes both procedural and declarative aspects of goals is necessary in order to reason about important properties of goals and to decouple plans from what these plans are meant to achieve. Second, lookahead deliberation about the effects of one choice of expansion over another is clearly desirable or even mandatory in many circumstances so as to guarantee goal achievability and to avoid undesired situations. Finally, a failure handling mechanism, suitably integrated with both declarative goals and planning, is required in order to model an adequate level of commitment to goals, as well as to be consistent with most real BDI implemented systems

handling, declarative goals, and planning

A BDI agent programming language with failur