There are two aspects of practical reasoning which present particular difficulties for current approaches to modelling practical reasoning through argumentation: temporal aspects, and the intrinsic worth of actions. Time is important because actions change the state of the world, we need to consider future states as well as past and present ones. Equally, it is often not what we do but the way that we do it that matters: the same future state may be reachable either through desirable or undesirable actions, and often also actions are done for their own sake rather than for the sake of their consequences. In this paper we will present a semantics for practical reasoning, based on a formalisation developed originally for reasoning about commands, in which actions and states are treated as of equal status. We will show how using these semantics facilitates the handling of the temporal aspects of practical reasoning, and enables, where appropriate, justification of actions without reference to their consequences.

Agent adaptability — the ability of agents to change behavioral strategies when it is beneficial to do so — is presumed to be an important part of the robustness of complex adaptive systems (CAS). But, determining when changing behaviors is advantageous for agents has proven quite challenging in CAS research, as sometimes behavioral change is necessary, but other times it can impose costs that exceed benefits. I present the results from experiments using an agent-based model (ABM) designed to discover thresholds after which behavioral flexibility leads to improved societal-level outcomes in groups of agents in dynamic environments. The first major result is that there are thresholds in both levels of flexibility in agent behavior and in levels of turbulence in the environment below and above which there are marked differences in utility gains for agents. In particular, relatively high flexibility leads to lower overall utility scores, as well as, surprisingly, decreased diversity and increased inequality between agents. The second result is that at very high levels of environmental turbulence, the effects of the environment alone on agent utility overshadow any benefits to agents from flexible behavior strategies. This suggests, counter-intuitively, that the best strategy for agents in very dynamic environments is simply to keep behavior constant. The third major result is that there is an interaction between agent behavior and the environment: high flexibility of other agents can effectively make an environment more "dynamic", which just fuels more flexibility, and leads to a scramble between different strategies with no utility gain. A final theoretical contribution of the paper is that the model is able to show drawbacks to flexibility without relying on costs to changing behavior, as is done in much of the literature on strategy change.

The relationship between equilibrium convergence to a uniform quality distribution and price is investigated in the Q-model, a self-organizing, evolutionary computational matching model of a fixed-price post-secondary higher education created by Ortmann and Slobodyan (2006). The Q-model is replicated with price equaling 100% its Ortmann and Slobodyan (2006) value, Varying the fixed price between 0% and 200% reveals thresholds at which the Q-model reaches different market clustering configurations. Results indicate structural market robustness to prices less than 100% and high sensitivity to prices greater than 100%.

Simulation techniques used to generate complex biological models are becoming promising research tools in oncology. Using a general Complex Adaptive Systems model that can be tailored to map various phenomena, here, we describe how this model applies to tumor growth. The multi-agent modeling environment is generated using Netlogo. The stochastic model consists of active objects including normal immune and cancer cells. The simulations conducted mimicked the tumor progression success and failure and the status of the tumor mass despite constant variations remained stable for an extended time. Furthermore, increasing the efficiency of the immune cells led to decreases in tumor cell numbers variable in both occurrence time and duration.

The web forum is a key tool in the building of new knowledge among students in Learning Management Systems. Students’ posted messages, in fact, build up a relationship network which supports a collaborative reflection about the forum topic. In this network two interaction levels can be distinguished. The former is the interaction between peers (the students), the latter between students and instructors (teachers and tutors). The role of the second interaction is particularly important as a feedback mechanism in the discussion dynamic but it is subjected to two kinds of limitations. The first one is the huge number of messages that makes difficult, for tutors and teachers, to quickly evaluate the progress of their students and the second one is the subjective bias of the tutors that influence the evaluation. In order to limit these two inefficiencies a multiagent system can be used to monitor such evolution and recognize the state in which the forum is. Such system is based on metrics derived from the textual and social network analysis that, feeding a rule engine, gives the instructor a more objective view of the forum evolution.

GLAIR (Grounded Layered Architecture with Integrated Reasoning) is a multi-layered cognitive architecture for embodied agents operating in real,virtual, or simulated environments containing other agents. The highest layer of the GLAIR Architecture, the Knowledge Layer (KL), contains the beliefs of the agent, and is the layer in which conscious reasoning, planning, and act selection is performed. The lowest layer of the GLAIR Architecture, the Sensori-Actuator Layer (SAL), contains the controllers of the sensors and effectors of the hardware or software robot. Between the KL and the SAL is the Perceptuo-Motor Layer (PML), which grounds the KL symbols in perceptual structures and subconscious actions, contains various registers for providing the agent's sense of situatedness in the environment, and handles translation and communication between the KL and the SAL. The motivation for the development of GLAIR has been "Computational Philosophy", the computational understanding and implementation of human-level intelligent behavior without necessarily being bound by the actual implementation of the human mind. Nevertheless, the approach has been inspired by human psychology and biology.

In this work we will present an integration of a query-answering argumentation approach with an abstract agent programming language. Agents will argumentatively reason via queries, using information of their mental components. Special context-based queries will be used to model the interaction between mental components. Deliberation and execution semantics of the proposed integration are presented.

An understanding of the policy and resource availability constraints under which others operate is important for effectively developing and resourcing plans in a multi-agent context. Such constraints (or norms) are not necessarily public knowledge, even within a team of collaborating agents. What is required are mechanisms to enable agents to keep track of who might have and be willing to provide the resources required for enacting a plan by modeling the policies of others regarding resource use, information provision, etc. We propose a technique that combines machine learning and argumentation for identifying and modeling the policies of others. Furthermore, we demonstrate the utility of this novel combination of techniques through empirical evaluation.

Time perception and inferences there from are of critical importance to many autonomous agents. But time is not perceived directly by any sensory organ. We argue that time is constructed by cognitive processes. Here we present a model for time perception that concentrates on succession and duration, and that generates these concepts and others, such as continuity, immediate present duration, and lengths of time. These concepts are grounded through the perceptual process itself. The LIDA cognitive model is used to illustrate these ideas.

Automated health behavior coaches (HBCs) potentially can provide a widely accessible, cost-effective means of promoting health behavior. Coaches are intelligent agents that “converse” with users, offering tailored feedback, advice, and empathy. Research subjects like coaches and comply with target behaviors, but interest and adherence wane over time. More research is needed on next-generation HBCs to improve coaching techniques, enhance user engagement, and extend adherence. However, the necessary technical tools and expertise reside in only a few research labs. In an effort to expand and accelerate research, we are developing an HBC Kit that will extend and specialize our more general Imp™ Kit. We propose 7 innovations for next-generation HBCs, demonstrate them in a lifestyle coach, and characterize authoring with the Imp Kit. We discuss planned extensions for the HBC Kit to enable a larger and more diverse community to create and evaluate a broader range of coaches.