One approach for artificially intelligent agents wishing to maximise some performance metric in a given domain is to learn from a collection of training data that consists of actions or decisions made by some expert, in an attempt to imitate that expert's style. We refer to this type of agent as an expert imitator. In this paper we investigate whether performance can be improved by combining decisions from multiple expert imitators. In particular, we investigate two existing approaches for combining decisions. The first approach combines decisions by employing ensemble voting between multiple expert imitators. The second approach dynamically selects the best imitator to use at runtime given the performance of the imitators in the current environment. We investigate these approaches in the domain of computer poker. In particular, we create expert imitators for limit and no limit Texas Hold'em and determine whether their performance can be improved by combining their decisions using the two approaches listed above.

Case-based problem-solving systems reason by retrieving relevant prior cases and adapting their solutions to fit new circumstances. The ability of case-based reasoning (CBR) to reason from ungeneralized episodes can benefit knowledge acquisition, but acquiring the needed case adaptation knowledge has proven challenging. This paper presents a method for alleviating this problem with just-in-time gathering of case adaptation knowledge, based on introspective reasoning and mining of Web knowledge sources. The approach combines knowledge planning with introspective reasoning to guide recovery from case adaptation failures and reinforcement learning to guide selection of knowledge sources. The failure recovery and knowledge source selection methods have been tested in three highly different domains with encouraging results. The paper closes with a discussion of limitations and future steps.

Another way to speed up a RL algorithm is by using Transfer Learning, a paradigm of machine learning that In this paper we propose to combine three AI techniques reuses knowledge accumulated in a previous task to speed up to speed up a Reinforcement Learning algorithm the learning of a novel, but related, target task [Taylor and in a Transfer Learning problem: Casebased Stone, 2009]. Reasoning, Heuristically Accelerated Reinforcement This paper investigates the use of the Case-Based Heuristically Learning and Neural Networks. To do Accelerated Reinforcement Learning (CB-HARL) algorithm so, we propose a new algorithm, called L3, which [Bianchi et al., 2009] as a means to transfer learning works in 3 stages: in the first stage, it uses Reinforcement acquired by one agent during its training in one problem to Learning to learn how to perform one another agent that has to learn how to solve a similar, but task, and stores the optimal policy for this problem more complex, problem. To do so, we propose a new algorithm, as a case-base; in the second stage, it uses a Neural called L3, which works in 3 stages: in the first stage, Network to map actions from one domain to actions it uses the Q-learning algorithm [Watkins, 1989] to learn how in the other domain and; in the third stage, it uses to perform one task, and stores the optimal policy for this the case-base learned in the first stage as heuristics problem as a case-base; in the second stage, it uses a Neural to speed up the learning performance in a related, Network to map actions from one domain to actions in but different, task. The RL algorithm used the other domain and; in the third stage, it uses the case-base in the first phase is the Q-learning and in the third learned in the first stage as heuristics in the CB-HARL algorithm, phase is the recently proposed Case-based Heuristically speeding up the learning process.

Computational narrative is a complex and interesting domain for exploring AI techniques that algorithmically analyze, understand, and most importantly, generate stories. This paper studies the importance of domain knowledge in story generation, and particularly in analogy-based story generation (ASG). Based on the construct of knowledge container in case-based reasoning, we present a theoretical framework for incorporating domain knowledge in ASG. We complement the framework with empirical results in our existing system Riu.

This requires, for Goal-driven autonomy (GDA) is a reflective model example, experts to anticipate what discrepancies can occur, of goal reasoning that controls the focus of an identify what goals can be formulated, and define their agent's planning activities by dynamically relative priority. However, few techniques have been resolving unexpected discrepancies in the world investigated for learning this knowledge, and those that do state, which frequently arise when solving tasks in learn only goal formulation knowledge (Weber et al. 2010; complex environments. GDA agents have Powell et al. 2011). This can be problematic; while these performed well on such tasks by integrating agents may perform well in simple environments, in others a methods for discrepancy recognition, explanation, domain expert might not know the (state) expectations for goal formulation, and goal management. However, executing every action in every state, nor which goal should they require substantial domain knowledge, be pursued to resolve every possible discrepancy, or even including what constitutes a discrepancy and how the space of all possible discrepancies.

There are a number of papers that use hill-climbing or k-We introduce a new nearest neighbor search algorithm. NN graphs for nearest neighbor search, but to the best of our The algorithm builds a nearest neighbor knowledge, using hill-climbing on k-NN graphs is a new idea.

The aim of this paper is to present the principles and results about case-based reasoning adapted to real- time interactive simulations, more precisely concerning retrieval mechanisms. The article begins by introducing the constraints involved in interactive multiagent-based simulations. The second section pre- sents a framework stemming from case-based reasoning by autonomous agents. Each agent uses a case base of local situations and, from this base, it can choose an action in order to interact with other auton- omous agents or users' avatars. We illustrate this framework with an example dedicated to the study of dynamic situations in football. We then go on to address the difficulties of conducting such simulations in real-time and propose a model for case and for case base. Using generic agents and adequate case base structure associated with a dedicated recall algorithm, we improve retrieval performance under time pressure compared to classic CBR techniques. We present some results relating to the performance of this solution. The article concludes by outlining future development of our project.

We present an efficient random decision tree algorithm for case-based reasoning systems. We combine this algorithm with a simple similarity measure based on domain knowledge to create a stronger hybrid algorithm. This combination is based on our general approach for combining lazy and eager learning methods. We evaluate the resulting algorithms on a case base of patient records in a palliative care domain. Our hybrid algorithm consistently produces a lower average error than the base algorithms.

Learning by observation allows a software agent to learn by watching an expert perform a task. This transfers the burden of training from the expert, who would traditionally need to program the agent, to the agent itself. Most existing approaches to learning by observation perform their observation in a purely passive manner. We propose a case-based reasoning agent that is able to observe passively but can also use mixed-initiative control to request assistance from the expert for difficult input problems. Our agent uses mixed-initiative case acquisition in the game of Tetris. We show that the agent is able to obtain cases it would not have been able to with passive observation alone, is able to improve its performance and places less burden on the expert.

Case-based reasoning (CBR) is an artificial intelligence problem solving and analysis methodology that retrieves and adapts previous experiences to fit new contexts. In CBR, a system's expertise is embodied in a library of past cases, rather than being encoded in classical rules. A new problem is solved by finding a similar past case, and reusing it in the new problem situation. Therefore, the knowledge and reasoning process used by an expert to solve the problem is not recorded, but is implicit in its solution. The CBR field has grown rapidly over the last few years, as seen by its increased share of papers at major conferences, available commercial tools, and successful applications in daily use.