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Autonomous agents in a multi-agent system work with each other to achieve their goals. However, In a partially observable world, current multi-agent systems are often less effective in achieving their goals. This limitation is due to the agents’ lack of reasoning about other agents and their mental states. Another factor is the agents’ inability to share required knowledge with other agents. This paper addresses the limitations by presenting a general approach for autonomous agents to work together in a multi-agent system. In this approach, an agent applies two main concepts: goal reasoning- to determine what goals to pursue and share; Theory of mind-to select an agent(s) for sharing goals and knowledge. We evaluate the performance of our multi-agent system in a Marine Life Survey Domain and compare it to another multi-agent system that randomly selects agent(s) to delegates its goals. 

Goal management in autonomous agents has been a problem of interest for a long time. Multiple goal operations are required to solve an agent goal management problem. For example, some goal operations include selection, change, formulation, delegation, monitoring. Many researchers from different fields developed several solution approaches with an implicit or explicit focus on goal operations. For example, some solution approaches include scheduling the agents’ goals, performing cost-benefit analysis to select/organize goals, agent goal formulation in unexpected situations. However, none of them explicitly shed light on the agents’ response when multiple goal operations occur simultaneously. This paper develops an algorithm to address agent goal management when multiple-goal operations co-occur and presents how such an interaction would improve agent goal management in different domains. 

Computational metacognition represents a cognitive systems perspective on high-order reasoning in integrated artificial systems that seeks to leverage ideas from human metacognition and from metareasoning approaches in artificial intelligence. The key characteristic is to declaratively represent and then monitor traces of cognitive activity in an intelligent system in order to manage the performance of cognition itself. Improvements in cognition then lead to improvements in behavior and thus performance. We illustrate these concepts with an agent implementation in a cognitive architecture called MIDCA and show the value of metacognition in problem-solving. The results illustrate how computational metacognition improves performance by changing cognition through meta-level goal operations and learning. 

Goal reasoning agents can solve novel problems by detecting an anomaly between expectations and observations, generating explanations about plausible causes for the anomaly, and formulating goals to remove the cause. Yet, not all anomalies represent problems. This paper addresses discerning the difference between benign anomalies and those that represent an actual problem for an agent. Furthermore, we present a new definition of the term “problem” in a goal reasoning context. This paper discusses the role of explanations and goal formulation in response to the developing problems and implements it; the paper also illustrates the above in a mine clearance domain and a labor relations domain. We also show the empirical difference between a standard planning agent, an agent that detects anomalies, and an agent that recognizes problems. 

Abstract Fishers reporting all of their catch is key to estimating population viabilities of vulnerable, highly migratory fish stocks. However, fishery managers find it difficult to ensure that this reporting behavior takes place consistently. Wild Atlantic salmon (Salmo salar) are a highly migratory and internationally contested species with a threatened conservation status. Greenland manages a fishery for Atlantic salmon, and its coastline serves as a key feeding ground in the life history of Atlantic salmon. However, salmon catch is underreported by fishers, even though they are required to report. Deterring noncompliant behavior with penalties and sending short message service (SMS) messages have been shown to increase compliance, but no known studies test their effect on compliance with catch reporting requirements. We evaluated two interventions for their effect on salmon catch reporting behavior among Greenland's salmon fishers. Salmon fishers were 41% more likely to report (p < 0.00) once a deterrence-based intervention was implemented. Fishers who received SMS reminders were 6% more likely to report salmon catch (p < 0.1). These results highlight the complementarity of nudges and command-and-control interventions to increase compliance with catch reporting requirements. 

In this paper we explore the potential of academic podcasting to effect positive change within academia and between academia and society. Building on the concept of “epistemic living spaces,” we consider how podcasting can change how we evaluate what is legitimate knowledge and methods for knowledge production, who has access to what privileges and power, the nature of our connections within academia and with other partners, and how we experience the constraints and opportunities of space and time. We conclude by offering a guide for others who are looking to develop their own academic podcasting projects and discuss the potential for podcasting to be formalized as a mainstream academic output. To listen to an abridged and annotated version of this paper, visit: https://soundcloud.com/conservechange/podcastinginacademia . 

An intelligent agent has many tasks and goals to achieve over specific time intervals. The goals may be assigned to it or the agent may generate its own goals. In either case, the number of goals at any given time may exceed its capacity to act upon concurrently. Therefore, an agent must prioritize the goals in chronological order as per their relative importance or significance. We show how an intelligent agent can estimate the trade-off between performance gains and resource costs to make smart choices concerning the goals it intends to achieve as opposed to selecting them in an arbitrary basis. We illustrate this method within the context of an intelligent cognitive architecture that supports various agent models. 

For over sixty years, the artificial intelligence and cognitive systems communities have represented problems to be solved as a combination of an initial state and a goal state along with some background domain knowledge. In this paper, I challenge this representation because it does not adequately capture the nature of a problem. Instead, a problem is a state of the world that limits choice in terms of potential goals or available actions. To begin to capture this view of a problem, a representation should include a characterization of the context that exists when a problem arises and an explanation that causally links the part of the context that contributes to the problem with a goal whose achievement constitutes a solution. The challenge to the research community is not only to represent such features but to design and implement agents that can infer them autonomously. 

For over sixty years, the artificial intelligence and cognitive systems communities have represented problems to be solved as a combination of an initial state and a goal state along with some background domain knowledge. In this paper, I challenge this representation because it does not adequately capture the nature of a problem. Instead, a problem is a state of the world that limits choice in terms of potential goals or available actions. To begin to capture this view of a problem, a representation should include a characterization of the context that exists when a problem arises and an explanation that causally links the part of the context that contributes to the problem with a goal whose achievement constitutes a solution. The challenge to the research community is not only to represent such features but to design and implement agents that can infer them autonomously.