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1. Problem recognition, explanation and goal formulation

   Kondrakunta, Sravya; Gogineni, Venkatsampath R.; Brown, Danielle; Molineaux, Matthew; Cox, Michael T. (August 2019, Proceedings of the Seventh Annual Conference on Advances in Cognitive Systems)

   Cox, Michael T. (Ed.)

   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. We claim that the task of discerning the difference between benign anomalies and those that represent an actual problem by an agent will increase its performance. 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 developing problems and implements the response. The paper illustrates goal formulation 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. 

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2. Problem recognition, explanation and goal formulation

   Kondrakunta, Sravya; Gogineni, Venkatsampath R.; Molineaux, Matthew; Cox, Michael T. (July 2021, Proceedings of the 2021 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'21))

   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. 

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3. The rational selection of goal operations and the integration of search strategies with goal-driven marine autonomy

   Kondrakunta, Sravya; Gogineni, Venkatsampath R.; Cox, Michael T.; Coleman, Demetris; Tan, Xiaobo; Lin, Tony; Hou, Mengxue; Zhang, Fumin; McQuarrie, Frank; Edwards, Catherine R. (November 2021, Proceedings of the Ninth Annual Conference on Advances in Cognitive Systems)

   Intelligent physical systems as embodied cognitive systems must perform high-level reasoning while concurrently managing an underlying control architecture. The link between cognition and control must manage the problem of converting continuous values from the real world to symbolic representations (and back). To generate effective behaviors, reasoning must include a capacity to replan, acquire and update new information, detect and respond to anomalies, and perform various operations on system goals. But, these processes are not independent and need further exploration. This paper examines an agent’s choices when multiple goal operations co-occur and interact, and it establishes a method of choosing between them. We demonstrate the benefits and discuss the trade offs involved with this and show positive results in a dynamic marine search task. 

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4. Multi-agent goal delegation

   Gogineni, Venkatsampath R.; Kondrakunta, Sravya; Cox, Michael T. (November 2021, Proceedings of the 9th Goal Reasoning Workshop)

   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. 

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5. Case-based explanations and goal specific resource estimations

   Gogineni, Venkatsampath R.; Kondrakunta, Sravya; Molineaux, Matthew; Cox, Michael T. (May 2020, Proceedings of the 33rd International Conference of the Florida Artificial Intelligence Research Society)

   Barták, Roman; Bell, Eric (Ed.)

   Autonomous agents often have sufficient resources to achieve the goals that are provided to them. However, in dynamic worlds where unexpected problems are bound to occur, an agent may formulate new goals with further resource requirements. Thus, agents should be smart enough to man-age their goals and the limited resources they possess in an effective and flexible manner. We present an approach to the selection and monitoring of goals using resource estimation and goal priorities. To evaluate our approach, we designed an experiment on top of our previous work in a complex mine-clearance domain. The agent in this domain formulates its own goals by retrieving a case to explain uncovered discrepancies and generating goals from the explanation. Finally, we compare the performance of our approach to two alternatives. 

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