Towards Flexible Teamwork

Many AI researchers are today striving to build agent teams for complex, dynamic multi-agent domains, with intended applications in arenas such as education, training, entertainment, information integration, and collective robotics. Unfortunately, uncertainties in these complex, dynamic domains obstruct coherent teamwork. In particular, team members often encounter differing, incomplete, and possibly inconsistent views of their environment. Furthermore, team members can unexpectedly fail in fulfilling responsibilities or discover unexpected opportunities. Highly flexible coordination and communication is key in addressing such uncertainties. Simply fitting individual agents with precomputed coordination plans will not do, for their inflexibility can cause severe failures in teamwork, and their domain-specificity hinders reusability. Our central hypothesis is that the key to such flexibility and reusability is providing agents with general models of teamwork. Agents exploit such models to autonomously reason about coordination and communication, providing requisite flexibility. Furthermore, the models enable reuse across domains, both saving implementation effort and enforcing consistency. This article presents one general, implemented model of teamwork, called STEAM. The basic building block of teamwork in STEAM is joint intentions (Cohen & Levesque, 1991b); teamwork in STEAM is based on agents' building up a (partial) hierarchy of joint intentions (this hierarchy is seen to parallel Grosz & Kraus's partial SharedPlans, 1996). Furthermore, in STEAM, team members monitor the team's and individual members' performance, reorganizing the team as necessary. Finally, decision-theoretic communication selectivity in STEAM ensures reduction in communication overheads of teamwork, with appropriate sensitivity to the environmental conditions. This article describes STEAM's application in three different complex domains, and presents detailed empirical results.Comment: See http://www.jair.org/ for an online appendix and other files accompanying this articl

Reinforcement Learning for Argumentation

Argumentation as a logical reasoning approach plays an important role in improving communication, increasing agree-ability, and resolving conflicts in multi-agent-systems (MAS). The present research aims to explore the effectiveness of argumentation in reinforcement learning of intelligent agents in terms of, outperforming baseline agents, learning transfer between argument graphs, and improving relevance and coherence of dialogue quality. This research developed `ARGUMENTO+' to encourage a reinforcement learning agent (RL agent) playing abstract argument game for improving performance against different baseline agents by using a newly proposed state representation in order to make each state unique. When attempting to generalise this approach to other argumentation graphs, the RL agent was not able to effectively identify the argument patterns that are transferable to other domains. In order to improve the effectiveness of the RL agent to recognise argument patterns, this research adopted a logic-based dialogue game approach with richer argument representations. In the DE dialogue game, the RL agent played against hard-coded heuristic agents and showed improved performance compared to the baseline agents by using a reward function that encourages the RL agent to win the game with minimum number of moves. This also allowed the RL agent to adopt its own strategy, make moves, and learn to argue. This thesis also presents a new reward function that makes the RL agent's dialogue more coherent and relevant than its opponents. The RL agent was designed to recognise argument patterns, i.e. argumentation schemes and evidence support sources, which can be related to different domains. The RL agent used a transfer learning method to generalise and transfer experiences and speed up learning

Metacognition and Reflection by Interdisciplinary Experts: Insights from Cognitive Science and Philosophy

Interdisciplinary understanding requires integration of insights from different perspectives, yet it appears questionable whether disciplinary experts are well prepared for this. Indeed, psychological and cognitive scientific studies suggest that expertise can be disadvantageous because experts are often more biased than non-experts, for example, or fixed on certain approaches, and less flexible in novel situations or situations outside their domain of expertise. An explanation is that experts’ conscious and unconscious cognition and behavior depend upon their learning and acquisition of a set of mental representations or knowledge structures. Compared to beginners in a field, experts have assembled a much larger set of representations that are also more complex, facilitating fast and adequate perception in responding to relevant situations. This article argues how metacognition should be employed in order to mitigate such disadvantages of expertise: By metacognitively monitoring and regulating their own cognitive processes and representations, experts can prepare themselves for interdisciplinary understanding. Interdisciplinary collaboration is further facilitated by team metacognition about the team, tasks, process, goals, and representations developed in the team. Drawing attention to the need for metacognition, the article explains how philosophical reflection on the assumptions involved in different disciplinary perspectives must also be considered in a process complementary to metacognition and not completely overlapping with it. (Disciplinary assumptions are here understood as determining and constraining how the complex mental representations of experts are chunked and structured.) The article concludes with a brief reflection on how the process of Reflective Equilibrium should be added to the processes of metacognition and philosophical reflection in order for experts involved in interdisciplinary collaboration to reach a justifiable and coherent form of interdisciplinary integration. An Appendix of “Prompts or Questions for Metacognition” that can elicit metacognitive knowledge, monitoring, or regulation in individuals or teams is included at the end of the article

The 1990 progress report and future plans

This document describes the progress and plans of the Artificial Intelligence Research Branch (RIA) at ARC in 1990. Activities span a range from basic scientific research to engineering development and to fielded NASA applications, particularly those applications that are enabled by basic research carried out at RIA. Work is conducted in-house and through collaborative partners in academia and industry. Our major focus is on a limited number of research themes with a dual commitment to technical excellence and proven applicability to NASA short, medium, and long-term problems. RIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at JPL and AI applications groups at all NASA centers

Proceedings of the Workshop on Change of Representation and Problem Reformulation

The proceedings of the third Workshop on Change of representation and Problem Reformulation is presented. In contrast to the first two workshops, this workshop was focused on analytic or knowledge-based approaches, as opposed to statistical or empirical approaches called 'constructive induction'. The organizing committee believes that there is a potential for combining analytic and inductive approaches at a future date. However, it became apparent at the previous two workshops that the communities pursuing these different approaches are currently interested in largely non-overlapping issues. The constructive induction community has been holding its own workshops, principally in conjunction with the machine learning conference. While this workshop is more focused on analytic approaches, the organizing committee has made an effort to include more application domains. We have greatly expanded from the origins in the machine learning community. Participants in this workshop come from the full spectrum of AI application domains including planning, qualitative physics, software engineering, knowledge representation, and machine learning

A Process Modelling Framework Based on Point Interval Temporal Logic with an Application to Modelling Patient Flows

This thesis considers an application of a temporal theory to describe and model the patient journey in the hospital accident and emergency (A&E) department. The aim is to introduce a generic but dynamic method applied to any setting, including healthcare. Constructing a consistent process model can be instrumental in streamlining healthcare issues. Current process modelling techniques used in healthcare such as flowcharts, unified modelling language activity diagram (UML AD), and business process modelling notation (BPMN) are intuitive and imprecise. They cannot fully capture the complexities of the types of activities and the full extent of temporal constraints to an extent where one could reason about the flows. Formal approaches such as Petri have also been reviewed to investigate their applicability to the healthcare domain to model processes. Additionally, to schedule patient flows, current modelling standards do not offer any formal mechanism, so healthcare relies on critical path method (CPM) and program evaluation review technique (PERT), that also have limitations, i.e. finish-start barrier. It is imperative to specify the temporal constraints between the start and/or end of a process, e.g., the beginning of a process A precedes the start (or end) of a process B. However, these approaches failed to provide us with a mechanism for handling these temporal situations. If provided, a formal representation can assist in effective knowledge representation and quality enhancement concerning a process. Also, it would help in uncovering complexities of a system and assist in modelling it in a consistent way which is not possible with the existing modelling techniques. The above issues are addressed in this thesis by proposing a framework that would provide a knowledge base to model patient flows for accurate representation based on point interval temporal logic (PITL) that treats point and interval as primitives. These objects would constitute the knowledge base for the formal description of a system. With the aid of the inference mechanism of the temporal theory presented here, exhaustive temporal constraints derived from the proposed axiomatic system’ components serves as a knowledge base. The proposed methodological framework would adopt a model-theoretic approach in which a theory is developed and considered as a model while the corresponding instance is considered as its application. Using this approach would assist in identifying core components of the system and their precise operation representing a real-life domain deemed suitable to the process modelling issues specified in this thesis. Thus, I have evaluated the modelling standards for their most-used terminologies and constructs to identify their key components. It will also assist in the generalisation of the critical terms (of process modelling standards) based on their ontology. A set of generalised terms proposed would serve as an enumeration of the theory and subsume the core modelling elements of the process modelling standards. The catalogue presents a knowledge base for the business and healthcare domains, and its components are formally defined (semantics). Furthermore, a resolution theorem-proof is used to show the structural features of the theory (model) to establish it is sound and complete. After establishing that the theory is sound and complete, the next step is to provide the instantiation of the theory. This is achieved by mapping the core components of the theory to their corresponding instances. Additionally, a formal graphical tool termed as point graph (PG) is used to visualise the cases of the proposed axiomatic system. PG facilitates in modelling, and scheduling patient flows and enables analysing existing models for possible inaccuracies and inconsistencies supported by a reasoning mechanism based on PITL. Following that, a transformation is developed to map the core modelling components of the standards into the extended PG (PG*) based on the semantics presented by the axiomatic system. A real-life case (from the King’s College hospital accident and emergency (A&E) department’s trauma patient pathway) is considered to validate the framework. It is divided into three patient flows to depict the journey of a patient with significant trauma, arriving at A&E, undergoing a procedure and subsequently discharged. Their staff relied upon the UML-AD and BPMN to model the patient flows. An evaluation of their representation is presented to show the shortfalls of the modelling standards to model patient flows. The last step is to model these patient flows using the developed approach, which is supported by enhanced reasoning and scheduling

Attentional processes involved in the development of set shifting and restricted and repetitive behaviours in young typically developing children and children with autism.

It is widely known that young typically developing (TD) children and many individuals with autism (ASD) perform poorly on executive function (EF) tasks. In pre- schoolers, these skills develop rapidly between the ages of 3 and 4 and are often measured through the Dimensional Change Card Sort (DCCS) task. This is also around the same time that restricted and repetitive behaviours (RRBs), a diagnostic characteristic for ASD, peak in typical development. These findings have led to an increasing interest in the relationship between EF skills and RRBs, but the studies have produced mixed findings. To our knowledge no meta-analyses have been carried out to examine the relationship between RRB scores and performance on EF measures. Moreover, no studies have yet pinpointed what it is about these skills or behaviours that make them associate so highly. This thesis therefore presents a series of experiments that firstly aim to examine the strength of the relationship between the behaviours and performance on EF tasks. Secondly, examine the relationship between different sub-groups of RRBs and various set shifting processes, such as the ability to shift away from dominant stimuli, and the ability to activate previously ignored stimuli. Finally, examine training implications for the skills by assessing if a short-term training program can improve the scores and possibly have an impact on the behaviours. In chapter 1, we conduct three meta-analyses to examine the relationship between RRB scores and performance on set shifting and inhibitory control tasks, as well as scores of EF parental report measures. We found significant correlations of medium strength in all three analyses. Moreover, whereas age and the type of RRB scale moderated the inhibitory control and parental report results; diagnosis, testing modality, and type of EF measure did not have an overall impact on the results. These findings suggest that the EF hypothesis may play a crucial role in the development of RRBs, or vice versa. Future research should focus on disentangling different EF measures to pinpoint what it is about the tasks that make them associate with the behaviours. In chapter 2, the focus is on set shifting, the individual EF skill that showed the strongest association with RRBs. Our aim in this chapter is to uncover what causes the correlations between the behaviours, and performance on the Wisconsin Card Sort Task, (WCST) but not the much simpler DCCS. We review the main theoretical frameworks that have attempted to explain two types of errors; the ability to shift away from dominant stimuli and the ability to activate previously irrelevant stimuli. Whereas research on the DCCS suggests that children find both errors difficult, research on the WCST suggests that adults find it more difficult to activate previously irrelevant responses. We argue that the different findings are not evidence for different developmental trajectories in children and adults. Instead, the tasks differ crucially in a way that only the design in the adult task isolates the errors properly and is consequently a pure measure of the two shifting processes. Our review concludes that both the ability to shift away from dominant stimuli and activate previously irrelevant stimuli play key roles in set shifting development, yet only the ability to activate previously irrelevant stimuli may be able to explain the high levels of RRBs in young TD children and individuals with ASD. In chapter 3 we assessed the two predictions in chapter 2 in more depth, through two experiments that compared different variations on the standard DCCS with a new method in which the relevant response is no longer available. We found an age-related shift in which pre-schoolers learned to pass all task versions around the age of four, offering support for the proposition that the ability to attend to previously irrelevant aspects of the environment play a key role in set shifting development. We also found support for the prediction that a child’s problems with activating a previously irrelevant cue (rule activation) may reveal biases of attention that explain the persistence of RRBs in typical and atypical development. We explain these through an attentional framework that suggests that the behaviours, and poor task performance is caused by difficulties with overriding automatic avoidance responses. These are responses that have been created over time as a person continuously ignores a response or an activity. In chapter 4, we evaluated the training literature to address why there are a lack of training studies on the topic. We also made suggestions for future training interventions. More specifically, we stress that EF interventions can be challenging and expensive, as they often require a high level of resources, such as parent training, or supervision of adults or teachers. Moreover, it has been questioned if such interventions can offer long-term training effectiveness, and generalise to situations outside of the lab. Future research should therefore develop a brief and cost-effective EF training program that requires low resources, and can be easily implemented in schools to examine the long-term effectiveness of this type of intervention, as well as if training can have an overall impact on RRB scores. In chapter 5, we examined the effectiveness of a brief training program to assess if pre-schoolers and children with ASD can be trained on tasks that measure their ability to activate previously irrelevant rules, and if training has the potential to influence the frequency and nature of their reported RRBs. We found highly significant training effects, and no change in set shifting performance in the control condition. We also found a small, yet not significant, decline in the RRB scores for the TD children after training. These findings propose that a brief rule activation training program may aid set shifting development and thereby be useful in a school setting. The RRB findings are less positive however, perhaps suggesting that to see an effect on the RRBs a training program may need to involve more sessions and run over a longer period of time. Overall, the results in this thesis provide evidence for the view that rule activation errors play a key role in the development of set shifting skills in pre-schoolers and individuals with ASD. Moreover, these errors may play a crucial role in the development of RRBs, or vice versa

Local Assets Management: A Case Study at the Education Office of DKI Jakarta Province

Research focus to know and describe management process of local assets. The approach used in this research is qualitative descriptive approach. This research was conducted at DKI Jakarta Provincial Education Office 2017. The sample used in qualitative research method is snowball. Data collection techniques use obsevation, interviews, and documentation. Data analysis with source triangulation and theory.The results showed: 1) Schools within the Education Department of the Provincial Government of DKI Jakarta in the procurement needs planning have not done the planning pattern through the study of the Needs Plan of the local asstes so that the goods needed but received are goods that are not needed. 2) Assets such as school land have not been certified on behalf of the DKI Government, or there are certain parties claiming part of their ownership, there are uncollected assets. From the aspect of maintenance because the maintenance planning has not done the pattern of maintenance planning through research of the Needs Plan of local assets Maintenance. 3) School does not understand the process of elimination in accordance with the provisions so that inventory items are not done through the process of removal. 4) Many inventory items at school are listed administratively but the facts can not physically be found. 5) No special officer is competent to carry out good governance so schools are recording assets trapped by teachers. Keywords: management, local assets

Partial-Order Planning with Concurrent Interacting Actions

In order to generate plans for agents with multiple actuators, agent teams, or distributed controllers, we must be able to represent and plan using concurrent actions with interacting effects. This has historically been considered a challenging task requiring a temporal planner with the ability to reason explicitly about time. We show that with simple modifications, the STRIPS action representation language can be used to represent interacting actions. Moreover, algorithms for partial-order planning require only small modifications in order to be applied in such multiagent domains. We demonstrate this fact by developing a sound and complete partial-order planner for planning with concurrent interacting actions, POMP, that extends existing partial-order planners in a straightforward way. These results open the way to the use of partial-order planners for the centralized control of cooperative multiagent systems